draw experimental setup

Chemix is an online editor for drawing lab diagrams and school experiment apparatus. Easy sketching for both students and teachers.

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Lab Diagrams Made Easy with Chemix

Are you a science teacher needing a quick way to explain lab procedures? Mr. Casanova, my high school chemistry teacher, would have loved this new digital tool. Chemix Lab Diagrams ( @chemixlab ) makes it easy for teachers to draw lab diagrams. You can then share those in a variety of formats. As of February 11, 2020, Chemix Lab Diagrams was still in beta, so you can look forward to improvements.

Why Chemix Lab Diagrams?

“I would use [Chemix Lab Diagrams] at the beginning of the school year. It would get students to recognize the lab equipment and how to use them,” says Efren Rodriguez ( @EfrenR ). Chemix is “an online editor for drawing science lab diagrams. It can be used for drawing school experiment apparatus. The app provides for easy sketching for both students and teachers.”

Listen to the creator of Chemix, Hai Mac, and Julia Winters (interviewer) share about Chemix :

• Website for creating diagrams of labs
• Show students how to set up an experiment
• Create diagrams for lab reports
• Save diagrams to the cloud
• Arrange images in Google Slides, then export the images to make an animated GIF

Check out this example from Dustin Draughn on a basic setup for an enzyme lab:

Some are already using it, such as @MrsWBio1 , to illustrate worksheets:

Do you think this is amazing? If so, you’ll love this collection of animated diagrams Efren Rodriguez put together.  And you can learn more about how to create animated GIFs of Chemix diagrams via this blog entry .

How Does Chemix Lab Diagrams Work?

Using Chemix is an easy process. When you go to their website, you’ll see a blank canvas with a beaker on a shelf. A left hand sidebar features a wealth of containers. If you know your way around a lab, you’ll recognize many of the items, such as test tubes, bung/stopper, and more. Want to use a boiling flask? A thiele tube or calorimetry cup? Chemix has you covered.

Just a quick trial… Still working on it… pic.twitter.com/6H5ejdT7YW — EfrenR (@EfrenR) January 21, 2020

Source: @EfrenR

Save images as JPGs, then convert them into animated GIFs. You can use a website like Gifmaker.me to make the animation. EfrenR has created a few animated GIFs which you can see in his Glide app ( watch his Glide tutorial ). Be sure to click on his “How To’s” tab in the app to see what Efren has come up with.

Students and teachers can explain what is happening in a lab. How can this be of benefit to students? Expecting students to create their own diagrams and to fine-tune them builds deeper conceptual understanding.

Deepening Conceptual Understanding

What leads to deeper learning is how students are able to use Chemix to teach themselves and others. It gives them control over a process they could only imagine before and makes their thinking about lab procedures visible. Teachers can check their students’ creations to verify understanding. This may assist them in deepening their learning as they transfer it to “ multiple, fact-rich contexts .”

Getting students to map out their own lab experiments empowers students. With Chemix, students can create their own lab materials and worksheets. This is important because it gives students control and mastery.

Miguel Guhlin

Transforming teaching, learning and leadership through the strategic application of technology has been Miguel Guhlin’s motto. Learn more about his work online at blog.tcea.org , mguhlin.org , and mglead.org /mglead2.org. Catch him on Mastodon @[email protected] Areas of interest flow from his experiences as a district technology administrator, regional education specialist, and classroom educator in bilingual/ESL situations. Learn more about his credentials online at mguhlin.net.

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A Figure One Web Tool for Visualization of Experimental Designs

The chi consortium.

2 NIH (Yuri Kotliarov, Julián Candia, Katherine Stagliano, Angélique Biancotto and John S. Tsang), US

This manuscript introduces a user-friendly, point and click open source and platform-independent software tool that aids the graphical representation of experimental studies. A graphical summary can give a high-level view of a study and represent in one illustration the important features of the data. Examples include sample collections, the time of each data collection, perturbations, and analysis performed. Graphical summaries can be useful in clarifying and documenting the complex relationships within an experiment by breaking down the component parts and expressing them visually. Commonly used cases for this tool include generating summary figures for presentation and publications. This tool was used either alone or in conjunction with other tools to generate schematic diagrams for talks and publications on several different on-going research projects.

(1) Overview

Introduction.

Experimental designs can be both complex and time-consuming. However, they provide useful information by improving transparency and documenting key experimental steps such as the collecting and the analysis of large datasets using different tests over time where subjects or animals are exposed to certain treatments [ 1 , 2 , 3 , 4 ].

Currently, there is a variety of drawing programs that can be used to draw diagrams, including Gimp ( https://www.gimp.org/ ), Inkscape ( https://inkscape.org/en/ ), PowerPoint ( https://products.office.com/en-us/powerpoint ) and TikZ ( https://sourceforge.net/projects/pgf/ ).

Experimental studies involving multiple tests, perturbation (disturbance on the biological system that causes it to change e.g.: Vaccination) and timepoints (points in time) can be complex and error prone to follow without the aid of an experimental design. We have seen a need for a web tool that can quickly generate schematic diagrams in a point and click manner. Here we present such a tool that avoids time-consuming tasks like drawing circles or arrows using primitive tools. This tool has already been successfully used to generate numerous experimental designs from several on-going research projects.

Although there is a minimal learning curve and an investment in development time, this tool was created with the aim of being easy to use, highly portable and containing useful features. There is not a method to hand-draw shapes, this tool is focused on specifying the exact locations of text (e.g.: labels, Timepoints, titles etc), lines, arrows and shapes by selecting coordinates. There is no programming functionality but the user is able to run loops that creates a series of lines, arrows, shapes with a varying parameter by selecting x-coordinates (time points) and y-coordinates.

The first step towards creating an experimental design requires the user to decide on whether to start from scratch or from one of the available templates.

Creating a Diagram

There are several options available in order to create a schematic of an experimental design. The first method involves using a pre-existing template, and second method involves using a blank canvas.

Using a Template

Our tool allows a user to create a diagram based on a pre-existing template ( Figure 1 ), then edit and add your own content which can save time and effort. The user can start a new diagram by editing an existing template, which consists of a URL that specifies the styles, settings, and layouts. The template can also serve as a useful reminder on how to create certain designs. Custom templates can be created by copying the URLs which then can be saved, reused and shared with collaborators. A selection of templates is available on the landing page. Click on the template link above each template that you would like to open and then navigate to the template window on your computer. Once the web page loads, the user would click the ‘Draw Figure!’ button to generate the visualization and then start editing. It is unlikely that you will find a template that precisely matches your needs, but you can select the closest fit and then modify it.

Creating a diagram based upon a pre-existing template.

Creating a Blank Canvas

The user starts by setting up the initial scope of the framework by selecting the total number of time points. Once the time points are selected the user can browse a library of shapes. The shapes and coordinates now represent the relationships between key steps such as the blood draws, time points, vaccinations, medication, transcriptomics, proteomics, etc. The first step requires the user to select and click on the ‘Draw from Scratch’ button or link to start a new visualization. Click on the button ‘Draw Figure!’ to create a blank canvas with a placeholder for the title, sub-title, captions, timepoints, x and y labels as shown in Figure 2 , default values for Timepoints are initially set to ten Timepoints.

Creating a diagram from scratch.

Creating Content

Users can select the number of timepoints or columns with the central slider. If a user needs to make adjustments, they can add or delete timepoints by moving and adjusting the slider.

Titles and labels can be added to the canvas by completing the automatically generated text boxes, found within the navigation side bar or remove the default text to leave it blank.

Click on ‘Add Item’ to add content. The user can place multiple shapes, icons and images by specifying precise coordinates which will reflect the locations across columns and rows on the canvas. Content can include different colours and sizes of arrows, circles, boxes, images, icons and rectangles. Rows and column coordinates will decide the location of content on a canvas and pressing the button ‘Draw Figure!’ will update the diagram.

Proof of Concept

As proof of concept we used the web tool to create a schematic diagram based on an actual experimental design for a clinical trial as shown in Figure 3 .

An example of a diagram output.

Output As PDF

Click on the “Output Plot To PDF” and follow the download link to a high resolution of your illustration.

Bookmark and Helpful Hints

The best way to avoid losing work is to save the URL early and save often. The bookmark feature allows the user to recreate the diagram from the URL and enables sharing with collaborators. We also implemented a dynamic interactive help system by clicking on the “Press for instruction button” which will then walk through and explain each feature.

Implementation and architecture

This web application is written using the shiny framework [ 5 ], a package from RStudio that can be used to build interactive web pages with R [ 6 ]. The code is split into two parts, (1) the user interface and (2) the server-side containing the logic. Numerous R packages were used including shinydashboard [ 7 ], ggplot2 [ 8 ]. See code for complete list.

Quality control

The software has been through several rounds of functional and usability testing. Each feature is tested by typing the input and examining the output making sure it operates according to the requirement specification. Usability testing was carried out by user feedback and manually checking completed diagrams. We have successfully created numerous diagrams with this tool to date. Support through this web tool is by feedback, bug reports and feature wishes from numerous users. The application runs in most modern web browsers, including Google Chrome, Safari, Firefox, and IE10+ and several operating systems, including Windows, Mac, Linux and Chrome.

(2) Availability

Operating system.

A platform-independent software package, compatible with modern web browsers (IE10+, Google Chrome, Firefox, Safari, etc.).

Programming language

Additional system requirements, dependencies.

Imports a number of R packages (see code for most up-to-date list).

Installation

We expect that most users will use the web tool directly from the website https://github.com/foocheung/figureone/blob/master/README_deploy.txt but users can also install this web tool from source code https://github.com/foocheung/figureone if required.

List of contributors

Software location, code repository.

Name: GitHub

Identifier: https://github.com/foocheung/figureone

License: Apache

Date published: 08/02/2018

(3) Reuse potential

As a standalone program, this web tool provides researchers with a way to draw experimental designs or summaries which in an intuitive, ‘clickable’ manner. This program has a high reuse potential “as is” and has already been a useful tool for several clinical projects from a wide spectrum of biology regardless of coding experience.

Funding statement:

This research was supported by the Intramural Research Program of the NIH, NIAID and CHI.

Competing Interests

The authors have no competing interests to declare.

• Ben Meacham's blog

Making Lab Diagrams Easier to Visualize

When it comes to student laboratory/apparatus setup, one thing is sure to help—visuals. However, many of us suffer from a disorder that makes all our test tubes resemble things we wish they didn’t. In addition, it is often hard to find that perfect image using the trusted Google search. However, it turns out there is a free and incredibly easy tool that allows you to assemble and customize almost any chemistry related setup you wish. Say hello to Chemix !

As described by its developer 1 ,

I use this intuitive app at least a couple times a week.  Sometimes it is just to capture an image of a beaker half full of water so that I can more easily draw particle diagrams on the board, while other times I use it to add clarity to my worksheets, labs, or assessments. 

Image 1 - Introduction to a question from an assessment of mine that Chemix helped improve

As a chemistry teacher, I strongly recommend you take five minutes to explore the awesome features of this useful tool. For Google Chrome users, you can easily add it as one of your Chrome apps so that you don't have to go to the website each time you want to make a diagram.

Image 2 -  Sample Chemix image I made that displays some of the apps cool features.

Check out this demo  for a brief glimpse of how powerful this little tool can be. I should mention that this demo was from 2009—Chemix can do so much more now!

1  Chemix website:  https://chemix.org (accessed 2/17/23)

All comments must abide by the ChemEd X Comment Policy , are subject to review, and may be edited. Please allow one business day for your comment to be posted, if it is accepted.

Such a time saver.

I was introduced to this website (by you!) at BCCE in Colorado a few years ago. It has been such a time saver!  I don't have to go searching for images in google anymore and I can personalize all the diagrams.  Thanks again!  

EdrawMax – Easy Diagram App

Make a diagram in 3 steps.

Free Chemistry Experiment Diagram Templates for Word, PowerPoint, PDF

Edraw is used as a chemistry experiment diagram software coming with ready-made chemistry experiment diagram templates that make it easy for anyone to create professional chemistry experiment diagram. The chemistry experiment diagram templates are easy to use and free. Edraw can also convert all these templates into PowerPoint, PDF or Word templates.

Download our Free Chemistry Experiment Diagram Templates in Software Package to Use However You Like

Edraw Chemistry Experiment Diagram Template

Start From Free Edraw Chemistry Experiment Diagram Template

Creating a chemistry experiment diagram in Edraw is easy. It only takes a few seconds to choose a basic template, add equipment and customize the appearance.

Free Download Chemistry Experiment Diagram Template

If you want to use a ready made template, go to chemistry experiment diagram templates page and choose the Chemistry Experiment Diagram that best suits you.

Create Diagram in 4 Easy Steps

PowerPoint Chemistry Experiment Diagram Template

Easy to Create Chemistry Experiment Diagram in PowerPoint

When you finish creating your chemistry experiment diagram in Edraw, one click on the Export button will transfer your drawing into MS PowerPoint presentation.

It's the easiest way to make chemistry experiment diagram in your study or teaching.

View a Simple PowerPoint Chemistry Experiment Diagram Template

Word Chemistry Experiment Diagram Template

Personalize your Chemistry Experiment Diagram and Give it the Look and Feel that You Want

To save the template as a design template, you need to download Edraw and edit it. All templates in the software gallery windows can be easily customized through changing color, theme and effect.

View a Word Chemistry Experiment Diagram Template

PDF Chemistry Experiment Diagram Template

Create Chemistry Experiment Diagram for PDF

All are simple, only clicking on the Export PDF button will convert your chemistry experiment diagram template into PDF.

Edraw - An Easy to Use Lab Diagram Drawing Tool

Discover why edraw is an excellent program to create chemistry experiment diagram. try edraw free ., get started you will love this easy-to-use diagram software.

EdrawMax is an advanced all-in-one diagramming tool for creating professional flowcharts, org charts, mind maps, network diagrams, UML diagrams, floor plans, electrical diagrams, science illustrations, and more. Just try it, you will love it!

EdrawMax App

3-step diagramming

How to Draw a Science Diagram

Why draw science diagrams.

While teaching science, there are several elements, objects, processes, or outputs/results that cannot be seen through naked eyes or felt merely by touching the substances. Some experiments require expensive equipment and materials, and many schools can’ t afford that.

This is where science diagrams come into the picture. With the help of diagrams, science teachers can easily illustrate the equipment, substances, and objects. In addition to this, the trainers also use directional arrows to represent the flow of processes that occur/take place before getting the final results.

For instance, chemistry professors cannot show molecules or atoms of certain substances to students. Likewise, these diagrams can help physics professors explain the magnetic fields of an electromagnet.

Another good example could be in the biology subject where the professors cannot dissect a human body to explain how it works. This is done with the help of diagrams where they draw all the arteries and veins to explain how they’re responsible for the blood circulation in a body.

Ways of Making Science Diagrams

Traditionally, students draw science diagrams manually on paper using a pencil, a couple of colored markers, a scale (ruler), and an eraser (to make minor adjustments and corrections), whereas lecturers do that on a blackboard or whiteboard using chalks or markers respectively.

Drawbacks of Traditional Method

Although the above method doesn’t require scholars and professors to carry any advanced equipment, the entire process is extremely time consuming and is prone to errors. This method also demands that the person holding a pencil, chalk, or marker has decent drawing skills failing to which, the diagram may not illustrate the object or process correctly upon completion.

Thankfully, it’s an IT era where things have become way easier with the help of software applications, and drawing science diagrams is not any exception either.

Today, there are several free and paid online diagramming tools that help you draw science diagrams quickly and easily as compared to the traditional method, without having you to be proficient in fine arts.

Such online tools have a complete library dedicated to a particular subject where all the required icons and symbols are present and can be used as needed. These programs also have lines and arrows called ‘Connectors’ to illustrate the relationship and workflow between the two elements. As a trainer or student, all you need to do is, drag and place the relevant symbols in the work area, and use the connectors to illustrate the relations or processes.

How to Make Science Diagrams Online?

The easiest way to draw science diagrams is with an efficient online tool. An example of one such diagramming solution is EdrawMax Online that is not only free to use, it also lets you export your creations to your preferred file format.

You can follow the steps below to easily create science diagrams using EdrawMax Online:

Go to https://www.edrawmax.com/online/ and sign-in to your account. If this is the first time you are using EdrawMax Online , you must create an account before you start to use it.

Ensure that New is selected from the left pane, scroll down the middle pane, select Science and Education , and from the main window in the right, click the thumbnail of your preferred science diagram template ( Molar Tooth Anatomy for this example). This opens a new document pre-populated with all the symbols and shapes relevant to the template you chose.

Alternatively, you can click the + icon from the main window to create a blank document and start drawing a custom science diagram from scratch.

Note: If you’ve subscribed for the premium membership, you also have access to a wide range of Free VIP templates with more complex diagrams.

Click Symbol Library from the top of the left pane, expand the Science category in the Library box that opens up, check the boxes for the subjects you want additional symbols of, and click OK to add all the symbols to Symbol Library .

Once the symbols are added to the Symbol Library , you can double-click (or click and drag) your preferred one to add it to the current diagram.

Go to File and click Save As or Export to save your document to Dropbox/Google Drive or export it as a PDF, DOCX, SVG, or PPTX file.

More Free Science Diagram Templates/Examples

In addition to picking from the EdrawMax Online portal itself, you can also download many other science diagram templates that the developer has made available for you. A few popular ones include:

Cell Diagram

– This template has a pre-built labeled diagram of a cell. The template is also fully editable and lets you make further modifications to the diagram as needed.

Food Web Diagram

– This template has pre-built symbols to illustrate how the entire food-chain works among animals.

Lab Apparatus List

– Especially for scholars, this template has labeled icons and symbols for various apparatuses used in a laboratory.

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Software to Draw Laboratory Apparatus

So I am preparing a blog post about chemistry for lay people and I have decided to start talking about the basics. It turns out that it would be super useful to create chemical apparatus on demand to illustrate the post. Such as this one:

I particularly like the clean vector image look. This is what I am looking for. A software that allows me to assemble these vector-graphics style 2D chemical apparatus as well as let me control the color (and texture, if possible) of the contents inside the flasks.

It is meaningful that the software contains correct laboratory glassware, preferably the classical stuff that is instantly recognizable like the bunsen burners and tripods rather than heat mantles, but that is me being picky. So far, anything goes.

• 4 $\begingroup$ If you don't find an answer here in a few days, try posting in softwarerecs.stackexchange.com $\endgroup$ –  DrMoishe Pippik Commented Nov 28, 2021 at 0:40
• $\begingroup$ @DrMoishePippik Thank you, I will try that as soon as a few days pass. On the other hand, do you think it would be harmful if I had both posts going simultaneously? $\endgroup$ –  urquiza Commented Nov 28, 2021 at 0:42
• 1 $\begingroup$ though some resent cross-posting, see meta.stackexchange.com/questions/64068/… $\endgroup$ –  DrMoishe Pippik Commented Nov 28, 2021 at 0:47
• 1 $\begingroup$ @urquiza: ChemDraw has a lot of templates for chemistry labs. $\endgroup$ –  ACR Commented Nov 28, 2021 at 1:38
• $\begingroup$ Likely you intend "clear" to apply to the graphic. Once you'll succeed on putting up the pics for your blog I would skip the rack and especially its joints as I am pretty sure they can be mixed up with pipes and valves. $\endgroup$ –  Alchimista Commented Nov 28, 2021 at 8:55

The short answer to this topic is that the typically used programs for this tasks vary in their coverage of of lab ware to prepare such illustrations and focus on drawing molecules, exporting molecules in machine readable (chemical) formats understood e.g., by databases, and performing some computations (e.g., averaged molecular and isotopic weight). Beside popularity of the programs if you want to share/collaborate with colleagues intermediate files, an additional point to consider are the graphic formats these programs offer for file I/O. Thus, a native export of the .svg formats understood e.g., by inkscape may be an advantage.

Without aim to provide an exhaustive description, the following examples may illustrate this with templates and building a distillation.

ACD ChemSketch contains quite a number of lab utensils in the template library. The free (as in free beer) version disables some functions which however are not relevant to drawing the beakers, flasks, etc. The representation of them did not change for decades, however the Windows program offers a native export as .png and .pdf . Recent releases improved interaction with wine to equally work well enough in Linux, too (this includes the current version 2021.1.3). It may take some tinkering to adjust the the individual pieces' orientation to build a setup.

A distillation:

ChemDraw offers templates which may be bitonal, or in color (see, e.g., here . Primarily written for Windows and Mac, with only varying success to be deployed in Linux, the program is widely used in academia and industry (definitively not for free as in free beer, often accessed within a campus license). The templates include parts aligned to fit better into the round bottom flasks. Among the export formats are .png and .svg . The later allows you e.g., to adjust the fill and stroke of the paths, or to remove the ace label (which actually is a trade mark of Ace Glass , NJ).

With many chemistry-relevant functions removed, the ChemDraw JS page allows to get familiar with them (stamp button opens a pull-down menu), to save the drawings in the native format (Structure -> Get .cdxml), as .png (-> Get image) or vector file (-> get .svg).

Some of the templates:

(image credit to a Russian blog post )

A distillation (color adjustments with Inkscape):

ChemDoodle is the youngest of these three sketchers with the largest number of lab utensils in the template library. Capable to interact with many chemistry-relevant file formats (including the public .cdxml of ChemDraw), the export of the graphics includes many options for round-trip edits, and export e.g., as either .png , or (optionally layered) vector format (.svg, .ps, .pdf) and anticipate their use in web pages and services like twitter. The purchase of one of their licenses offers the user to choose between a program for Windows, or Mac, or Linux; this includes the option to shuttle the license key among the operating systems.

An overview of the chemistry templates:

With light retouches in inkscape, an illustration of a short-path distillation:

Contrasting to the programs above, chemix 's focus is about drawing a lab setup exported either as bitmap or vector file. (Maybe drawing organic structures will be added.) By number, the inventory of lab utensils (still) is smaller than e.g., the one offered in ChemDoodle, though it contains material absent in the other collections (e.g., a waterless condenser, or the GHS symbols).

In addition to standard options to move and scale the objects, there are interesting details in handling the objects like (incomplete list):

• joining the elements is guided by snap-points like magnets
• both color and height of liquids in the containers may be adjusted within the interface, including boiling-like bubbles
• a tilt of the container automatically affects the meniscus of the liquid
• changing the height of the lab boy affects the scissoring

The green arc sign in the illustration below mark utensils you access when entering a paid subscription. Based on their twitter feed , there is continuing development and addition of utensils for this application running remotely in your web browser.

An illustration:

The comparison with the utensils in the lab may reveal differences between the sets offered (e.g., Chemix' missing pressure release for a distillation present in Chemsketch and ChemDraw's sets/how you should mount safely a distillation) may be seen; thus, design with care for detail.

• 1 $\begingroup$ @Buttonwood That was a stupendous answer. However, I tried Chemix and wasn't able to put the boiling liquid mask to my fluids inside the glassware. Is that a premium feature? $\endgroup$ –  urquiza Commented Dec 7, 2021 at 22:00
• $\begingroup$ @urquiza The seventh category «Accessories» contains bubbles, which equally are available in the free section. As colour, «default» seems to blend nicely if this rectangle is stashed behind the beaker/round bottom flask in question. This may require some adjustment of rows (i.e., height) and bubbles per row (i.e., width) of the «bubble rectangle» to fit for the (adjustable height) of liquid in the container (which may be moved as usual by the mouse, too). $\endgroup$ –  Buttonwood Commented Dec 12, 2021 at 1:56

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Creating a schematic diagram of an experiment

I am looking to create a experimental schematic similar to this image, and am wondering what graphic design program I should use.

Here are my criteria:

• Easy to use for a beginner
• Easy to modify particular components when the experimental setup changes
• Saveable in a variety of formats, perhaps including svg

I am curious as to both free and paid options. Thanks all!

• information-graphics

• 1 This is a perfect candidate for Adobe Illustrator, but Illustrator may not be the easiest (or cheapest) option. –  Scott Commented Apr 8, 2013 at 18:22
• 1 You can get a single-app membership for Illustrator for around $20 a month, which is not bad at all. There is also a Student and Teacher edition that is cheaper, so maybe you or a student/teacher friend can look into that. –  Yisela Commented Apr 8, 2013 at 20:36
• If you're willing to use 3D for this, Google Sketchup is an easy-to-use 3D modelling program, particularly useful for creating constructions such as these. Anyone can learn to use it in a couple of hours, so you might want to give it a shot. It doesn't match your third criterium, but it does match the first two perfectly. –  paddotk Commented Apr 10, 2013 at 9:37
• As for the saving, you can save it to 3d formats or 2D formats such as .png, .jpg and a few others. –  paddotk Commented Apr 10, 2013 at 9:37

5 Answers 5

"Easy to use" is a bit of a challenge is that's going to depend on a whole lot of criteria.

That said, I'd suggest Inkscape . It's open source, so is no cost to give it a try. It's not as robust as Adobe Illustrator, so would argue that it's simpler to learn. And it can certainly save out in many formats, including it's native format SVG.

Inkscape as already suggested if you don't intend to use it often or want to spend several 100 dollars.

Buy the design edition of Adobe CS6. I say buy the design package because at the rate of just purchasing Illustrator it is a waste. You can buy the design package which would include Photoshop, InDesign, Illustrator, and Acrobat. You could always find someone with an old copy or key they are not using and go that route.

It would be a great time to learn CSS3 shapes and maybe even add some animation. Just a quick example of shapes here

Any solution may prove timely but it really all depends on what you plan to get out of it or time you want to invest.

I'm surprised that no one mentioned Microsoft Powerpoint.... To me, it looks like that was the program it was made with based on the border thicknesses, that all the shapes are readily available in the insert shape menu, and has the same default font as powerpoint.

Even if it was not made with this program, Powerpoint is a great program for simple graphics like the above. I am a student in the engineering/sciences and Powerpoint is widely used for presentations, posters, and papers. The upside is that it's pretty easy to learn and available all over the place

The downside is that it is limited to simpler graphics and it is pretty expensive if you are not planning to use the rest of the programs in Microsoft Office's bundle.

Just a thought, I know this thread is old, but it might help someone somewhere!

• Good point. Many may not recommend Powerpoint because it's not suitable for print production, but this may be possible in PowerPoint... output desires may alter that though. –  Scott Commented Jun 15, 2015 at 21:47
• Just as an added point to this comment, you can tweak ppt so that it does export images at 300dpi @ 100% ... see here hos.ufl.edu/meteng/HansonWebpagecontents/… - I have mine upgraded for things like pie charts and graphs for printed presentations, but any image can be exported this way. –  Mark Read Commented Jun 15, 2015 at 23:28

Mondrian is a free vector graphics web app like Adobe Illustrator or Inkscape, and runs in your browser http://mondrian.io/ .

It's open-source. So if you're into lasers and technical stuff, you can build your own copy via Github .

give chemix.org a shot, fit all criteria perfectly.

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Mastering Research: The Principles of Experimental Design

In a world overflowing with information and data, how do we differentiate between mere observation and genuine knowledge? The answer lies in the realm of experimental design. At its core, experimental design is a structured method used to investigate the relationships between different variables. It's not merely about collecting data, but about ensuring that this data is reliable, valid, and can lead to meaningful conclusions.

The significance of a well-structured research process cannot be understated. From medical studies determining the efficacy of a new drug, to businesses testing a new marketing strategy, or environmental scientists assessing the impact of climate change on a specific ecosystem – a robust experimental design serves as the backbone. Without it, we run the risk of drawing flawed conclusions or making decisions based on erroneous or biased information.

The beauty of experimental design is its universality. It's a tool that transcends disciplines, bringing rigor and credibility to investigations across fields. Whether you're in the world of biotechnology, finance, psychology, or countless other domains, understanding the tenets of experimental design will ensure that your inquiries are grounded in sound methodology, paving the way for discoveries that can shape industries and change lives.

Core principles

Types of experimental designs, steps in designing an experiment, pitfalls and challenges, case studies, tools and software, future progress, how experimental design has evolved over time.

Delving into the annals of scientific history, we find that experimental design, as a formalized discipline, is relatively young. However, the spirit of experimentation is ancient, sewn deeply into the fabric of human curiosity. As early as Ancient Greece, rudimentary experimental methods were employed to understand natural phenomena . Yet, the structured approach we recognize today took centuries to develop.

The Renaissance era witnessed a surge in scientific curiosity and methodical investigation . This period marked a shift from reliance on anecdotal evidence and dogmatic beliefs to empirical observation. Notably, Sir Francis Bacon , during the early 17th century, championed the empirical method, emphasizing the need for systematic data collection and analysis.

But it was during the late 19th and early 20th centuries that the discipline truly began to crystallize. The burgeoning fields of psychology, agriculture, and biology demanded rigorous methods to validate their findings. The introduction of statistical methods and controlled experiments in agricultural research set a benchmark for research methodologies across various disciplines.

From its embryonic stages of simple observation to the sophisticated, statistically driven methodologies of today, experimental design has been shaped by the demands of the times and the relentless pursuit of truth by generations of researchers. It has evolved from mere intuition-based inquiries to a framework of control, randomization, and replication, ensuring that our conclusions stand up to the strictest scrutiny.

Key figures and their contributions

When charting the evolution of experimental design, certain luminaries stand tall, casting long shadows of influence that still shape the field today. Let's delve into a few of these groundbreaking figures:

• Contribution: Often heralded as the father of modern statistics, Fisher introduced many concepts that form the backbone of experimental design. His work in the 1920s and 1930s laid the groundwork for the design of experiments.
• Legacy: Fisher's introduction of the randomized controlled trial, analysis of variance ( ANOVA ), and the principle of maximum likelihood estimation revolutionized statistics and experimental methodology. His book, The Design of Experiments , remains a classic reference in the field.
• Contribution: A prolific figure in the world of statistics, Pearson developed the method of moments , laying the foundation for many statistical tests.
• Legacy: Pearson's chi-squared test is one of the many techniques he introduced, which researchers still widely use today to test the independence of categorical variables.
• Contribution: Together, they conceptualized the framework for the theory of hypothesis testing , which is a staple in modern experimental design.
• Legacy: Their delineation of Type I and Type II errors and the introduction of confidence intervals have become fundamental concepts in statistical inference.
• Contribution: While better known as a nursing pioneer, Nightingale was also a gifted statistician. She employed statistics and well-designed charts to advocate for better medical practices and hygiene during the Crimean War .
• Legacy: Nightingale's application of statistical methods to health underscores the importance of data in decision-making processes and set a precedent for evidence-based health policies.
• Contribution: Box made significant strides in the areas of quality control and time series analysis.
• Legacy: The Box-Jenkins (or ARIMA) model for time series forecasting and the Box-Behnken designs for response surface methodology are testaments to his lasting influence in both experimental design and statistical forecasting.

These trailblazers, among many others, transformed experimental design from a nascent field of inquiry into a robust and mature discipline. Their innovations continue to guide researchers and inform methodologies, bridging the gap between curiosity and concrete understanding.

Randomization: ensuring each subject has an equal chance of being in any group

Randomization is the practice of allocating subjects or experimental units to different groups or conditions entirely by chance. This means each participant, or experimental unit, has an equal likelihood of being assigned to any specific group or condition.

Why is this method of assignment held in such high regard, and why is it so fundamental to the research process? Let's delve into the pivotal role randomization plays and its overarching importance in maintaining the rigor of experimental endeavors.

• Eliminating Bias: By allocating subjects randomly, we prevent any unintentional bias in group assignments. This ensures that the groups are more likely to be comparable in all major respects. Without randomization, researchers might, even inadvertently, assign certain types of participants to one group over another, leading to skewed results.
• Balancing Unknown Factors: There are always lurking variables that researchers might be unaware of or unable to control. Randomization helps in ensuring that these unobserved or uncontrolled variables are equally distributed across groups, thereby ensuring that the groups are comparable in all major respects.
• Foundation for Statistical Analysis: Randomization is the bedrock upon which much of statistical inference is built. It allows researchers to make probabilistic statements about the outcomes of their studies. Without randomization, many of the statistical tools employed in analyzing experimental results would be inappropriate or invalid.
• Enhancing External Validity: A randomized study increases the chances that the results are generalizable to a broader population. Because participants are randomly selected, the findings can often be extrapolated to similar groups outside the study.

While randomization is a powerful tool, it's not without its challenges. For instance, in smaller samples, randomization might not always guarantee perfectly balanced groups. Moreover, in some contexts, like when studying the effects of a surgical technique, randomization might be ethically challenging.

Nevertheless, in the grand scheme of experimental design, randomization remains a gold standard. It's a bulwark against biases, both known and unknown, ensuring that research conclusions are drawn from a foundation of fairness and rigor.

Replication: repeating the experiment to ensure results are consistent

At its essence, replication involves conducting an experiment again, under the same conditions, to verify its results. It's like double-checking your math on a complex equation—reassuring yourself and others that the outcome is consistent and not just a random occurrence or due to unforeseen errors.

So, what makes this practice of repetition so indispensable to the research realm? Let's delve deeper into the role replication plays in solidifying and authenticating scientific insights.

• Verifying Results: Even with the most rigorous experimental designs, errors can creep in, or unusual random events can skew results. Replicating an experiment helps confirm that the findings are genuine and not a result of such anomalies.
• Reducing Uncertainty: Every experiment comes with a degree of uncertainty. By replicating the study, this uncertainty can be reduced, providing a clearer picture of the phenomenon under investigation.
• Uncovering Variability: Results can vary due to numerous reasons—slight differences in conditions, experimental materials, or even the subjects themselves. Replication can help identify and quantify this variability, lending more depth to the understanding of results.
• Building Scientific Consensus: Replication is fundamental in building trust within the scientific community. When multiple researchers, possibly across different labs or even countries, reproduce the same results, it strengthens the validity of the findings.
• Enhancing Generalizability: Repeated experiments, especially when performed in different locations or with diverse groups, can ensure that the results apply more broadly and are not confined to specific conditions or populations.

While replication is a robust tool in the researcher's arsenal, it isn't always straightforward. Sometimes, especially in fields like psychology or medicine, replicating the exact conditions of the original study can be challenging. Furthermore, in our age of rapid publication, there might be a bias towards novel findings rather than repeated studies, potentially undervaluing the importance of replication.

In conclusion, replication stands as a sentinel of validity in experimental design. While one experiment can shed light on a phenomenon, it's the repeated and consistent results that truly illuminate our understanding, ensuring that what we believe is based not on fleeting chance but on reliable and consistent evidence.

Control: keeping other variables constant while testing the variable of interest

In its simplest form, control means keeping all factors and conditions, save for the variable being studied, consistent and unchanged. It's akin to setting a stage where everything remains static, allowing the spotlight to shine solely on the lead actor: our variable of interest.

What exactly elevates this principle to such a paramount position in the scientific realm? Let's unpack the fundamental reasons that underscore the indispensability of control in experimental design.

• Isolating the Variable of Interest: With numerous factors potentially influencing an experiment, it's crucial to ensure that the observed effects result solely from the variable being studied. Control aids in achieving this isolation, ensuring that extraneous variables don't cloud the results.
• Eliminating Confounding Effects: Without proper control, other variables might interact with the variable of interest, leading to misleading or confounded outcomes. By keeping everything else constant, control ensures the purity of results.
• Enhancing the Credibility of Results: When an experiment is well-controlled, its results become more trustworthy. It demonstrates that the researcher has accounted for potential disturbances, leading to a more precise understanding of the relationship between variables.
• Facilitating Replication: A well-controlled experiment provides a consistent framework, making it easier for other researchers to replicate the study and validate its findings.
• Aiding in Comparisons: By ensuring that all other variables remain constant, control allows for a clearer comparison between different experimental groups or conditions.

Maintaining strict control is not always feasible, especially in field experiments or when dealing with complex systems. In such cases, researchers often rely on statistical controls or randomization to account for the influence of extraneous variables.

In the grand tapestry of experimental research, control serves as the stabilizing thread, ensuring that the patterns we observe are genuine reflections of the variable under scrutiny. It's a testament to the meticulous nature of scientific inquiry, underscoring the need for precision and care in every step of the experimental journey.

Completely randomized design

The Completely Randomized Design (CRD) is an experimental setup where all the experimental units (e.g., participants, plants, animals) are allocated to different groups entirely by chance. There's no stratification, clustering, or blocking. In essence, every unit has an equal opportunity to be assigned to any group.

Here are the advantages that make it a favored choice for many researchers:

• Simplicity: CRD is easy to understand and implement, making it suitable for experiments where the primary goal is to compare the effects of different conditions or interventions without considering other complicating factors.
• Flexibility: Since the only criterion is random assignment, CRD can be employed in various experimental scenarios, irrespective of the number of conditions or experimental units.
• Statistical Robustness: Due to its random nature, the CRD is amenable to many statistical analyses. When the assumptions of independence, normality, and equal variances are met, CRD allows for straightforward application of techniques like ANOVA to discern the effects of different conditions.

However, like any tool in the research toolkit, the Completely Randomized Design doesn't come without its caveats. It's crucial to acknowledge the limitations and considerations that accompany CRD, ensuring that its application is both judicious and informed.

• Efficiency: In situations where there are recognizable subgroups or blocks within the experimental units, a CRD might not be the most efficient design. Variability within blocks could overshadow the effects of different conditions.
• Environmental Factors: If the experimental units are spread across different environments or conditions, these uncontrolled variations might confound the effects being studied, leading to less precise or even misleading conclusions.
• Size: In cases where the sample size is small, the sheer randomness of CRD might result in uneven group sizes, potentially reducing the power of the study.

The Completely Randomized Design stands as a testament to the power of randomness in experimental research. While it might not be the best fit for every scenario, especially when there are known sources of variability, it offers a robust and straightforward approach for many research questions. As with all experimental designs, the key is to understand its strengths and limitations, applying it judiciously based on the specifics of the research at hand.

Randomized block design

The Randomized Block Design (RBD) is an experimental configuration where units are first divided into blocks or groups based on some inherent characteristic or source of variability. Within these blocks, units are then randomly assigned to different conditions or categories. Essentially, it's a two-step process: first, grouping similar units, and then, randomizing assignments within these groups.

Here are the positive attributes of the Randomized Block Design that underscore its value in experimental research:

• Control Over Variability: By grouping similar experimental units into blocks, RBD effectively reduces the variability that might otherwise confound the results. This enhances the experiment's power and precision.
• More Accurate Comparisons: Since conditions are randomized within blocks of similar units, comparisons between different effects become more accurate and meaningful.
• Flexibility: RBD can be employed in scenarios with any number of conditions and blocks. Its flexible nature makes it suitable for diverse experimental needs.

While the merits of the Randomized Block Design are widely recognized, understanding its potential limitations and considerations is paramount to ensure that research outcomes are both insightful and grounded in reality:

• Complexity: Designing and analyzing an RBD can be more complex than simpler designs like CRD. It requires careful consideration of how to define blocks and how to randomize conditions within them.
• Assumption of Homogeneity: RBD assumes that the variability within blocks is less than the variability between them. If this assumption is violated, the design might lose its efficiency.
• Increased Sample Size: To maintain power, RBD might necessitate a larger sample size, especially if there are numerous blocks.

The Randomized Block Design stands as an exemplary method to combine the best of both worlds: the robustness of randomization and the sensitivity to inherent variability. While it might demand more meticulous planning and design, its capacity to deliver more refined insights makes it a valuable tool in the realm of experimental research.

Factorial design

A factorial design is an experimental setup where two or more independent variables, or factors, are simultaneously tested, not only for their individual effects but also for their combined or interactive effects. If you imagine an experiment where two factors are varied at two levels each, you would have a 2x2 factorial design, resulting in four unique experimental conditions.

Here are the advantages you should consider regarding this methodology:

• Efficiency: Instead of conducting separate experiments for each factor, researchers can study multiple factors in a single experiment, conserving resources and time.
• Comprehensive Insights: Factorial designs allow for the exploration of interactions between factors. This is crucial because in real-world situations, factors often don't operate in isolation.
• Generalizability: By varying multiple factors simultaneously, the results tend to be more generalizable across a broader range of conditions.
• Optimization: By revealing how factors interact, factorial designs can guide practitioners in optimizing conditions for desired outcomes.

No methodology is without its nuances, and while factorial designs boast numerous strengths, they come with their own set of limitations and considerations:

• Complexity: As the number of factors or levels increases, the design can become complex, demanding more experimental units and potentially complicating data analysis.
• Potential for Confounding: If not carefully designed, there's a risk that effects from one factor might be mistakenly attributed to another, especially in higher-order factorial designs.
• Resource Intensive: While factorial designs can be efficient, they can also become resource-intensive as the number of conditions grows.

The factorial design stands out as an essential tool for researchers aiming to delve deep into the intricacies of multiple factors and their interactions. While it requires meticulous planning and interpretation, its capacity to provide a holistic understanding of complex scenarios renders it invaluable in experimental research.

Matched pair design

A Matched Pair Design , also known simply as a paired design, is an experimental setup where participants are grouped into pairs based on one or more matching criteria, often a specific characteristic or trait. Once matched, one member of each pair is subjected to one condition while the other experiences a different condition or control. This design is particularly powerful when comparing just two conditions, as it reduces the variability between subjects.

As we explore the advantages of this design, it becomes evident why it's often the methodology of choice for certain investigative contexts:

• Control Over Variability: By matching participants based on certain criteria, this design controls for variability due to those criteria, thereby increasing the experiment's sensitivity and reducing error.
• Efficiency: With a paired approach, fewer subjects may be required compared to completely randomized designs, potentially making the study more time and resource-efficient.
• Direct Comparisons: The design facilitates direct comparisons between conditions, as each pair acts as its own control.

As with any research methodology, the Matched Pair Design, despite its distinct advantages, comes with inherent limitations and critical considerations:

• Matching Complexity: The process of matching participants can be complicated, demanding meticulous planning and potentially excluding subjects who don't fit pairing criteria.
• Not Suitable for Multiple Conditions: This design is most effective when comparing two conditions. When there are more than two conditions to compare, other designs might be more appropriate.
• Potential Dependency Issues: Since participants are paired, statistical analyses must account for potential dependencies between paired observations.

The Matched Pair Design stands as a great tool for experiments where controlling for specific characteristics is crucial. Its emphasis on paired precision can lead to more reliable results, but its effective implementation requires careful consideration of the matching criteria and statistical analyses. As with all designs, understanding its nuances is key to leveraging its strengths and mitigating potential challenges.

Covariate design

A Covariate Design , also known as Analysis of Covariance (ANCOVA), is an experimental approach wherein the main effects of certain independent variables, as well as the effect of one or more covariates, are considered. Covariates are typically variables that are not of primary interest to the researcher but may influence the outcome variable. By including these covariates in the analysis, researchers can control for their effect, providing a clearer picture of the relationship between the primary independent variables and the outcome.

While many designs aim for clarity by isolating variables, the Covariate Design embraces and controls for the intricacies, presenting a series of compelling advantages. As we unpack these benefits, the appeal of incorporating covariates into experimental research becomes increasingly evident:

• Increased Precision: By controlling for covariates, this design can lead to more precise estimates of the main effects of interest.
• Efficiency: Including covariates can help explain more of the variability in the outcome, potentially leading to more statistically powerful results with smaller sample sizes.
• Flexibility: The design offers the flexibility to account for and control multiple extraneous factors, allowing for more comprehensive analyses.

Every research approach, no matter how robust, comes with its own set of challenges and nuances. The Covariate Design is no exception to this rule:

• Assumption Testing: Covariate Design requires certain assumptions to be met, such as linearity and homogeneity of regression slopes, which, if violated, can lead to misleading results.
• Complexity: Incorporating covariates adds complexity to the experimental setup and the subsequent statistical analysis.
• Risk of Overadjustment: If not chosen judiciously, covariates can lead to overadjustment, potentially masking true effects or leading to spurious findings.

The Covariate Design stands out for its ability to refine experimental results by accounting for potential confounding factors. This heightened precision, however, demands a keen understanding of the design's assumptions and the intricacies involved in its implementation. It serves as a powerful option in the researcher's arsenal, provided its complexities are navigated with knowledge and care.

Designing an experiment requires careful planning, an understanding of the underlying scientific principles, and a keen attention to detail. The essence of a well-designed experiment lies in ensuring both the integrity of the research and the validity of the results it yields. The experimental design acts as the backbone of the research, laying the foundation upon which meaningful conclusions can be drawn. Given the importance of this phase, it's paramount for researchers to approach it methodically. To assist in this experimental setup, here's a step-by-step guide to help you navigate this crucial task with precision and clarity.

• Identify the Research Question or Hypothesis: Before delving into the experimental process, it's crucial to have a clear understanding of what you're trying to investigate. This begins with defining a specific research question or formulating a hypothesis that predicts the outcome of your study. A well-defined research question or hypothesis serves as the foundation for the entire experimental process.
• Choose the Appropriate Experimental Design: Depending on the nature of your research question and the specifics of your study, you'll need to choose the most suitable experimental design. Whether it's a Completely Randomized Design, a Randomized Block Design, or any other setup, your choice will influence how you conduct the experiment and analyze the data.
• Select the Subjects/Participants: Determine who or what will be the subjects of your study. This could range from human participants to animal models or even plants, depending on your field of study. It's vital to ensure that the selected subjects are representative of the larger population you aim to generalize to.
• Allocate Subjects to Different Groups: Once you've chosen your participants, you'll need to decide how to allocate them to different experimental groups. This could involve random assignment or other methodologies, ensuring that each group is comparable and that the effects of confounding variables are minimized.
• Implement the Experiment and Gather Data: With everything in place, conduct the experiment according to your chosen design. This involves exposing each group to the relevant conditions and then gathering data based on the outcomes you're measuring.
• Analyze the Data: Once you've collected your data, it's time to dive into the numbers. Using statistical tools and techniques, analyze the data to determine whether there are significant differences between your groups, and if your hypothesis is supported.
• Interpret the Results and Draw Conclusions: Data analysis will provide you with statistical outcomes, but it's up to you to interpret what these numbers mean in the context of your research question. Draw conclusions based on your findings, and consider their implications for your field and future research endeavors.

By following these steps, you can ensure a structured and systematic approach to your experimental research, paving the way for insightful and valid results.

Confounding variables: external factors that might influence the outcome

One of the most common challenges faced in experimental design is the presence of confounding variables. These are external factors that unintentionally vary along with the factor you are investigating, potentially influencing the outcome of the experiment. The danger of confounding variables lies in their ability to provide alternative explanations for any observed effect, thereby muddying the waters of your results.

For instance, if you were investigating the effect of a new drug on blood pressure and failed to control for factors like caffeine intake or stress levels, you might mistakenly attribute changes in blood pressure to the drug when they were actually caused by these other uncontrolled factors.

Properly identifying and controlling for confounding variables is essential. Failure to do so can lead to false conclusions and misinterpretations of data. Addressing them either through the experimental design itself, like by using randomization or matched groups, or in the analysis phase, such as through statistical controls, ensures that the observed effects can be confidently attributed to the variable or condition being studied rather than to extraneous influences.

External validity: making sure results can be generalized to broader contexts

A paramount challenge in experimental design is guaranteeing external validity. This concept refers to the degree to which the findings of a study can be generalized to settings, populations, times, and measures different from those specifically used in the study.

The dilemma often arises in highly controlled environments, such as laboratories. While these settings allow for precise conditions and minimized confounding variables, they might not always reflect real-world scenarios. For instance, a study might find a specific teaching method effective in a quiet, one-on-one setting. However, if that same method doesn't perform as well in a busy classroom with 30 students, the study's external validity becomes questionable.

For researchers, the challenge is to strike a balance. While controlling for potential confounding variables is paramount, it's equally crucial to ensure the experimental conditions maintain a certain degree of real-world relevance. To enhance external validity, researchers may use strategies such as diversifying participant pools, varying experimental conditions, or even conducting field experiments. Regardless of the approach, the ultimate goal remains: to ensure the experiment's findings can be meaningfully applied in broader, real-world contexts.

Ethical considerations: ensuring the safety and rights of participants

Any experimental design undertaking must prioritize the well-being, dignity, and rights of participants. Upholding these values not only ensures the moral integrity of any study but also is crucial in ensuring the reliability and validity of the research .

All participants, whether human or animal, are entitled to respect and their safety should never be placed in jeopardy. For human subjects, it's imperative that they are adequately briefed about the research aims, potential risks, and benefits. This highlights the significance of informed consent, a process where participants acknowledge their comprehension of the study and willingly agree to participate.

Beyond the initiation of the experiment, ethical considerations continue to play a pivotal role. It's vital to maintain the privacy and confidentiality of the participants, ensuring that the collected data doesn't lead to harm or stigmatization. Extra caution is needed when experiments involve vulnerable groups, such as children or the elderly. Furthermore, researchers should be equipped to offer necessary support or point towards professional help should participants experience distress because of the experimental procedures. It's worth noting that many research institutions have ethical review boards to ensure all experiments uphold these principles, fortifying the credibility and authenticity of the research process.

The Stanford Prison Experiment (1971)

The Stanford Prison Experiment , conducted in 1971 by psychologist Philip Zimbardo at Stanford University, stands as one of the most infamous studies in the annals of psychology. The primary objective of the experiment was to investigate the inherent psychological mechanisms and behaviors that emerge when individuals are placed in positions of power and subordination. To this end, volunteer participants were randomly assigned to roles of either prison guards or inmates in a simulated prison environment.

Zimbardo's design sought to create an immersive environment, ensuring that participants genuinely felt the dynamics of their assigned roles. The mock prison was set up in the basement of Stanford's psychology building, complete with cells and guard quarters. Participants assigned to the role of guards were provided with uniforms, batons, and mirrored sunglasses to prevent eye contact. Those assigned as prisoners wore smocks and stocking caps, emphasizing their status. To enhance the realism, an unannounced "arrest" was made for the "prisoners" at their homes by the local police department. Throughout the experiment, no physical violence was permitted; however, the guards were allowed to establish their own rules to maintain order and ensure the prisoners attended the daily counts.

Scheduled to run for two weeks, the experiment was terminated after only six days due to the extreme behavioral transformations observed. The guards rapidly became authoritarian, implementing degrading and abusive strategies to maintain control. In contrast, the prisoners exhibited signs of intense emotional distress, and some even demonstrated symptoms of depression. Zimbardo himself became deeply involved, initially overlooking the adverse effects on the participants. The study's findings highlighted the profound impact that situational dynamics and perceived roles can have on behavior. While it was severely criticized for ethical concerns, it underscored the depths to which human behavior could conform to assigned roles, leading to significant discussions on the ethics of research and the power dynamics inherent in institutional settings.

The Stanford Prison Experiment is particularly relevant to experimental design for these reasons:

• Control vs. Realism: One of the challenging dilemmas in experimental design is striking a balance between controlling variables and maintaining ecological validity (how experimental conditions mimic real-world situations). Zimbardo's study attempted to create a highly controlled environment with the mock prison but also sought to maintain a sense of realism by arresting participants at their homes and immersing them in their roles. The consequences of this design, however, were unforeseen and extreme behavioral transformations.
• Ethical Considerations: A cornerstone of experimental design involves ensuring the safety, rights, and well-being of participants. The Stanford Prison Experiment is often cited as an example of what can go wrong when these principles are not rigorously adhered to. The psychological distress faced by participants wasn't anticipated in the original design and wasn't adequately addressed during its execution. This oversight emphasizes the critical importance of periodic assessment of participants' well-being and the flexibility to adapt or terminate the study if adverse effects arise.
• Role of the Researcher: Zimbardo's involvement and the manner in which he became part of the experiment highlight the potential biases and impacts a researcher can have on an experiment's outcome. In experimental design, it's crucial to consider the researcher's role and minimize any potential interference or influence they might have on the study's results.
• Interpretation of Results: The aftermath of the experiment brought forth critical discussions on how results are interpreted and presented. It emphasized the importance of considering external influences, participant expectations, and other confounding variables when deriving conclusions from experimental data.

In essence, the Stanford Prison Experiment serves as a cautionary tale in experimental design. It underscores the importance of ethical considerations, participant safety, the potential pitfalls of high realism without safeguards, and the unintended consequences that can emerge even in well-planned experiments.

Meselson-Stahl Experiment (1958)

The Meselson-Stahl Experiment , conducted in 1958 by biologists Matthew Meselson and Franklin Stahl , holds a significant place in molecular biology. The duo set out to determine the mechanism by which DNA replicates, aiming to understand if it follows a conservative, semi-conservative, or dispersive model.

Utilizing Escherichia coli (E. coli) bacteria, Meselson and Stahl grew cultures in a medium containing a heavy isotope of nitrogen, 15 N, allowing the bacteria's DNA to incorporate this heavy isotope. Subsequently, they transferred the bacteria to a medium with the more common 14 N isotope and allowed it to replicate. By using ultracentrifugation, they separated DNA based on density, expecting distinct bands on a gradient depending on the replication model.

The observed patterns over successive bacterial generations revealed a single band that shifted from the heavy to light position, supporting the semi-conservative replication model. This meant that during DNA replication, each of the two strands of a DNA molecule serves as a template for a new strand, leading to two identical daughter molecules. The experiment's elegant design and conclusive results provided pivotal evidence for the molecular mechanism of DNA replication, reshaping our understanding of genetic continuity.

The Meselson-Stahl Experiment is particularly relevant to experimental design for these reasons:

• Innovative Techniques: The use of isotopic labeling and density gradient ultracentrifugation was pioneering, showcasing the importance of utilizing and even developing novel techniques tailored to address specific scientific questions.
• Controlled Variables: By methodically controlling the growth environment and the nitrogen sources, Meselson and Stahl ensured that any observed differences in DNA density were due to the replication mechanism itself, and not extraneous factors.
• Direct Comparison: The experiment design allowed for direct comparison between the expected results of different replication models and the actual observed outcomes, facilitating a clear and decisive conclusion.
• Clarity in Hypothesis: The researchers had clear expectations for the results of each potential replication model, which helped in accurately interpreting the outcomes.

Reflecting on the Meselson-Stahl Experiment, it serves as an exemplar in experimental biology. Their meticulous approach, combined with innovative techniques, answered a fundamental biological question with clarity. This experiment not only resolved a significant debate in molecular biology but also showcased the power of well-designed experimental methods in revealing nature's intricate processes.

The Hawthorne Studies (1920s-1930s)

The Hawthorne Studies , conducted between the 1920s and 1930s at Western Electric's Hawthorne plant in Chicago, represent a pivotal shift in organizational and industrial psychology. Initially intended to study the relationship between lighting conditions and worker productivity, the research evolved into a broader investigation of the various factors influencing worker output and morale. These studies have since shaped our understanding of human relations and the socio-psychological aspects of the workplace.

The Hawthorne Studies comprised several experiments, but the most notable were the "relay assembly tests" and the "bank wiring room studies." In the relay assembly tests, researchers made various manipulations to the working conditions of a small group of female workers, such as altering light levels, giving rest breaks, and changing the length of the workday. The intent was to identify which conditions led to the highest levels of productivity. Conversely, the bank wiring room studies were observational in nature. Here, the researchers aimed to understand the group dynamics and social structures that emerged among male workers, without any experimental manipulations.

Surprisingly, in the relay assembly tests, almost every change—whether it was an improvement or a return to original conditions—led to increased worker productivity. Even when conditions were reverted to their initial state, worker output remained higher than before. This puzzling phenomenon led researchers to speculate that the mere act of being observed and the knowledge that one's performance was being monitored led to increased effort and productivity, a phenomenon now referred to as the Hawthorne Effect . The bank wiring room studies, on the other hand, shed light on how informal group norms and social relations could influence individual productivity, often more significantly than monetary incentives.

These studies challenged the then-dominant scientific management approach, which viewed workers primarily as mechanical entities whose productivity could be optimized through physical and environmental adjustments. Instead, the Hawthorne Studies highlighted the importance of psychological and social factors in the workplace, laying the foundation for the human relations movement in organizational management.

The Hawthorne Studies are particularly relevant to experimental design for these reasons:

• Observer Effect: The Hawthorne Studies introduced the idea that the mere act of observation could alter participants' behavior. This has significant implications for experimental design, emphasizing the need to account for and minimize observer-induced changes in behavior.
• Complexity of Human Behavior: While the initial focus was on physical conditions (like lighting), the results demonstrated that human behavior and performance are influenced by a myriad of interrelated factors. This underscores the importance of considering psychological, social, and environmental variables when designing experiments.
• Unintended Outcomes: The unintended discovery of the Hawthorne Effect exemplifies that experimental outcomes can sometimes diverge from initial expectations. Researchers should remain open to such unexpected findings, as they can lead to new insights and directions.
• Evolution of Experimental Focus: The shift from purely environmental manipulations to observational studies in the Hawthorne research highlights the flexibility required in experimental design. As new findings emerge, it's crucial for researchers to adapt their methodologies to better address evolving research questions.

In summary, the Hawthorne Studies serve as a testament to the evolving nature of experimental research and the profound effects that observation, social dynamics, and psychological factors can have on outcomes. They highlight the importance of adaptability, holistic understanding, and the acknowledgment of unexpected results in the realm of experimental design.

Michelson-Morley Experiment (1887)

The Michelson-Morley Experiment , conducted in 1887 by physicists Albert A. Michelson and Edward W. Morley , is considered one of the foundational experiments in the world of physics. The primary aim was to detect the relative motion of matter through the hypothetical luminiferous aether, a medium through which light was believed to propagate.

Michelson and Morley designed an apparatus known as the interferometer . This device split a beam of light so that it traveled in two perpendicular directions. After reflecting off mirrors, the two beams would recombine, and any interference patterns observed would indicate differences in their travel times. If the aether wind existed, the Earth's motion through the aether would cause such an interference pattern. The experiment was conducted at different times of the year, considering Earth's motion around the sun might influence the results.

Contrary to expectations, the experiment found no significant difference in the speed of light regardless of the direction of measurement or the time of year. This null result was groundbreaking. It effectively disproved the existence of the luminiferous aether and paved the way for the theory of relativity introduced by Albert Einstein in 1905 , which fundamentally changed our understanding of time and space.

The Michelson-Morley Experiment is particularly relevant to experimental design for these reasons:

• Methodological Rigor: The precision and care with which the experiment was designed and conducted set a new standard for experimental physics.
• Dealing with Null Results: Rather than being discarded, the absence of the expected result became the main discovery, emphasizing the importance of unexpected outcomes in scientific research.
• Impact on Theoretical Foundations: The experiment's findings had profound implications, showing that experiments can challenge and even overturn prevailing theoretical frameworks.
• Iterative Testing: The experiment was not just a one-off. Its repeated tests at different times underscore the value of replication and varied conditions in experimental design.

Through their meticulous approach and openness to unexpected results, Michelson and Morley didn't merely answer a question; they reshaped the very framework of understanding within physics. Their work underscores the essence of scientific inquiry: that true discovery often lies not just in confirming our hypotheses, but in uncovering the deeper truths that challenge our prevailing notions. As researchers and scientists continue to push the boundaries of knowledge, the lessons from this experiment serve as a beacon, reminding us of the potential that rigorous, well-designed experiments have in illuminating the mysteries of our universe.

Borlaug's Green Revolution (1940s-1960s)

The Green Revolution , spearheaded by agronomist Norman Borlaug between the 1940s and 1960s, represents a transformative period in agricultural history. Borlaug's work focused on addressing the pressing food shortages in developing countries. By implementing advanced breeding techniques, he aimed to produce high-yield, disease-resistant, and dwarf wheat varieties that would boost agricultural productivity substantially.

To achieve this, Borlaug and his team undertook extensive crossbreeding of wheat varieties. They employed shuttle breeding —a technique where crops are grown in two distinct locations with different planting seasons. This not only accelerated the breeding process but also ensured the new varieties were adaptable to varied conditions. Another innovation was to develop strains of wheat that were "dwarf," ensuring that the plants, when loaded with grains, didn't become too tall and topple over—a common problem with high-yielding varieties.

The resulting high-yield, semi-dwarf, disease-resistant wheat varieties revolutionized global agriculture. Countries like India and Pakistan, which were on the brink of mass famine, witnessed a dramatic increase in wheat production. This Green Revolution saved millions from starvation, earned Borlaug the Nobel Peace Prize in 1970, and altered the course of agricultural research and policy worldwide.

The Green Revolution is particularly relevant to experimental design for these reasons:

• Iterative Testing: Borlaug's approach highlighted the significance of continual testing and refining. By iterating breeding processes, he was able to perfect the wheat varieties more efficiently.
• Adaptability: The use of shuttle breeding showcased the importance of ensuring that experimental designs account for diverse real-world conditions, enhancing the global applicability of results.
• Anticipating Challenges: By focusing on dwarf varieties, Borlaug preempted potential problems, demonstrating that foresight in experimental design can lead to more effective solutions.
• Scalability: The work wasn't just about creating a solution, but one that could be scaled up to meet global demands, emphasizing the necessity of scalability considerations in design.

The Green Revolution exemplifies the profound impact well-designed experiments can have on society. Borlaug's strategies, which combined foresight with rigorous testing, reshaped global agriculture, underscoring the potential of scientific endeavors to address pressing global challenges when thoughtfully and innovatively approached.

Experimental design has undergone a transformation over the years. Modern technology plays an indispensable role in refining and streamlining experimental processes. Gone are the days when researchers solely depended on manual calculations, paper-based data recording, and rudimentary statistical tools. Today, advanced software and tools provide accurate, quick, and efficient means to design experiments, collect data, perform statistical analysis, and interpret results.

Several tools and software are at the forefront of this technological shift in experimental design:

• Minitab: A popular statistical software offering tools for various experimental designs including factorials, response surface methodologies, and optimization techniques.
• R: An open-source programming language and environment tailored for statistical computing and graphics. Its extensibility and comprehensive suite of statistical techniques make it a favorite among researchers.
• JMP: Developed by SAS , it is known for its interactive and dynamic graphics. It provides a powerful suite for design of experiments and statistical modeling.
• Design-Expert: A software dedicated to experimental design and product optimization. It's particularly useful for response surface methods.
• SPSS: A software package used for statistical analysis, it provides advanced statistics, machine learning algorithms, and text analysis for researchers of all levels.
• Python (with libraries like SciPy and statsmodels): Python is a versatile programming language and, when combined with specific libraries, becomes a potent tool for statistical analysis and experimental design.

One of the primary advantages of using these software tools is their capability for advanced statistical analysis. They enable researchers to perform complex computations within seconds, something that would take hours or even days manually. Furthermore, the visual representation features in these tools assist in understanding intricate data patterns, correlations, and other crucial aspects of data. By aiding in statistical analysis and interpretation, software tools eliminate human errors, provide insights that might be overlooked in manual analysis, and significantly speed up the research process, allowing scientists and researchers to focus on drawing accurate conclusions and making informed decisions based on the data.

The world of experimental research is continually evolving, with each new development promising to reshape how we approach, conduct, and interpret experiments. The central tenets of experimental design—control, randomization, replication—though fundamental, are being complemented by sophisticated techniques that ensure richer insights and more robust conclusions.

One of the most transformative forces in experimental design's future landscape is the surge of artificial intelligence (AI) and machine learning (ML) technologies . Historically, the design and analysis of experiments have depended on human expertise for selecting factors to study, setting the levels of these factors, and deciding on the number and order of experimental runs. With AI and ML's advent, many of these tasks can be automated, leading to optimized experimental designs that might be too complex for manual formulation. For instance, machine learning algorithms can predict potential outcomes based on vast datasets, guiding researchers in choosing the most promising experimental conditions.

Moreover, AI-driven experimental platforms can dynamically adapt during the course of the experiment, tweaking conditions based on real-time results, thereby leading to adaptive experimental designs. These adaptive designs promise to be more efficient, as they can identify and focus on the most relevant regions of the experimental space, often requiring fewer experimental runs than traditional designs. By harnessing the power of AI and ML, researchers can uncover complex interactions and nonlinearities in their data that might have otherwise gone unnoticed.

Furthermore, the convergence of AI and experimental design holds tremendous potential for areas like drug development and personalized medicine. By analyzing vast genetic datasets, AI algorithms can help design experiments that target very specific biological pathways or predict individual patients' responses to particular treatments. Such personalized experimental designs could dramatically reduce the time and cost of bringing new treatments to market and ensuring that they are effective for the intended patient populations.

In conclusion, the future of experimental design is bright, marked by rapid advancements and a fusion of traditional methods with cutting-edge technologies. As AI and machine learning continue to permeate this field, we can expect experimental research to become more efficient, accurate, and personalized, heralding a new era of discovery and innovation.

In the ever-evolving landscape of research and innovation, experimental design remains a cornerstone, guiding scholars and professionals towards meaningful insights and discoveries. As we reflect on its past and envision its future, it's clear that experimental design will continue to play an instrumental role in shaping the trajectory of numerous disciplines. It will be instrumental in harnessing the full potential of emerging technologies, driving forward scientific understanding, and solving some of the most pressing challenges of our time. With a rich history behind it and a promising horizon ahead, experimental design stands as a testament to the human spirit's quest for knowledge, understanding, and innovation.

Header image by Gorodenkoff .