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Last updated on 2024年5月4日

How do you design experiments that are replicable and robust?

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Experimental design is the process of planning and conducting a systematic investigation to test a hypothesis or answer a research question. It is a crucial skill for scientists, engineers, and innovators who want to generate reliable and valid results. But how do you design experiments that are replicable and robust? Here are some tips and best practices to help you create and execute high-quality experiments.

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William Marks

PhD Neuroscientist specializing in circuit neuroscience, electrophysiology, and neurovirology

1 Define your objectives

Before you start designing your experiment, you need to have a clear and specific idea of what you want to achieve. What is the problem or gap that you want to address? What is the main question or hypothesis that you want to test? What are the expected outcomes or implications of your experiment? Having a well-defined objective will help you focus your experiment and avoid unnecessary or irrelevant variables.

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William Marks

PhD Neuroscientist specializing in circuit neuroscience, electrophysiology, and neurovirology
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It is important to recognize that strictly hypothesis driven science and more exploratory approaches can both be valuable, but have their own pros and cons, and areas in which they are more or less appropriate. For example, exploratory approaches can yield a new, but broad finding which must then be honed using a more finely tuned hypothesis-driven approach. A one-size fits all approach to experimental design may be tempting, however experimental designs must be carefully crafted to avoid the introduction of biases or false suppositions into the underlying question. Make sure to carefully consider the nature of the gap in the literature you intend to address as you clarify your questions and consider how to design your experiments.

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2 Choose your design

Depending on your objective, you may need to choose a different type of experimental design. For example, if you want to compare the effects of two or more treatments or interventions, you may use a randomized controlled trial (RCT) or a quasi-experimental design. If you want to explore the causal relationships between variables, you may use a factorial design or a regression discontinuity design. If you want to measure the change in an outcome over time, you may use a longitudinal design or a repeated measures design. Choosing the appropriate design will help you control for confounding factors and increase the validity of your results.

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Franklin I.

Medical student at Warren Alpert Medical School of Brown University
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Choosing the right research design hinges on several factors:, 1) Research Question: Match the design to the question—cross-sectional for prevalence, RCTs for testing interventions. 2) Resource Availability: Resources like budget, time, and expertise dictate the feasibility of complex designs like RCTs, while observational studies may suit limited resources. 3) Ethical Considerations: Ethics can limit design choices, especially in clinical research where withholding proven therapy for an RCT could be unethical, making observational studies more suitable..

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3 Select your sample

Another important aspect of experimental design is selecting your sample, which is the group of units or participants that you will study. Your sample should be representative of the population that you want to generalize your results to, and it should be large enough to detect the effect size that you expect. You should also use a random or stratified sampling method to avoid selection bias and ensure that your sample is balanced and diverse.

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4 Set your parameters

Once you have your design and sample, you need to set your parameters, which are the specific details of how you will conduct your experiment. For example, you need to decide how you will measure your outcome variable, how you will manipulate or assign your treatment variable, how you will ensure that your experiment is ethical and safe, and how you will handle missing or incomplete data. You should also pre-register your protocol and analysis plan to increase the transparency and credibility of your experiment.

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5 Run your experiment

The next step is to run your experiment, which means collecting and recording your data according to your parameters. You should follow the standard operating procedures (SOPs) and quality assurance (QA) guidelines for your field or discipline, and use appropriate tools and instruments to ensure the accuracy and consistency of your data. You should also monitor and document any deviations or problems that may occur during your experiment, and report them in your results.

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6 Analyze and communicate your results

The final step is to analyze and communicate your results, which means applying statistical methods and techniques to test your hypothesis or answer your question. You should use the appropriate tests and models for your design and data, and report the effect sizes, confidence intervals, and p-values of your findings. You should also interpret and discuss your results in the context of your objective and previous literature, and acknowledge the limitations and implications of your experiment. You should also share your data, code, and materials with other researchers and stakeholders, and publish or present your results in a clear and accessible format.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you design experiments that are replicable and robust?

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1 Define your objectives

2 Choose your design

3 Select your sample

4 Set your parameters

5 Run your experiment

6 Analyze and communicate your results

7 Here’s what else to consider

Experimental Design

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