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Last updated on 2024年5月17日

How do you avoid common pitfalls in experimental design?

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Experimental design is the process of planning and conducting a scientific investigation to test a hypothesis, answer a question, or explore a phenomenon. It involves choosing the appropriate methods, variables, controls, and measurements to ensure the validity, reliability, and reproducibility of the results. However, experimental design is not always straightforward or flawless. There are many common pitfalls that can compromise the quality and interpretation of your data, such as confounding factors, selection bias, measurement errors, and poor communication. In this article, you will learn how to avoid some of these pitfalls and improve your experimental design skills.

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Natalie Rodrigue

Skilled Biostatistician & Epidemiologist - Mentor, Experienced, Knowledgeable & Passionate Instructor - DoE - Expert…
Ron Leavitt, PhD

Expert Scientist at GSK | Assay Development | Next Generation Sequencing (NGS) | Biomarker Scientist | Experimental…

1 Define your research question and hypothesis

Before you start designing your experiment, you need to have a clear and specific research question and hypothesis. A research question is the problem or issue that you want to investigate, and a hypothesis is a tentative answer or explanation that you want to test. Your research question and hypothesis should be based on existing literature, theory, or observation, and they should be testable, measurable, and relevant. A well-defined research question and hypothesis will help you narrow down your scope, select your variables, and design your procedures.

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Ron Leavitt, PhD

Expert Scientist at GSK | Assay Development | Next Generation Sequencing (NGS) | Biomarker Scientist | Experimental Design | Bioinformatics | Data Analysis | Molecular Biology
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A robust experimental design hinges on a well-defined research question and hypothesis. Without a clear understanding of what you aim to investigate and test, your experiment risks becoming unfocused and inconclusive. Take the time to craft a precise research question that encapsulates the essence of your inquiry, and formulate a hypothesis that provides a testable prediction. This foundational step sets the stage for the rest of your experimental journey.

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2 Choose your variables and controls

Variables are the factors that can affect the outcome of your experiment. They can be classified into three types: independent, dependent, and controlled. The independent variable is the one that you manipulate or change to test its effect on the dependent variable, which is the one that you measure or observe as the outcome. The controlled variables are the ones that you keep constant or minimize their variation to reduce confounding factors. Confounding factors are the variables that can influence the dependent variable but are not part of your hypothesis. They can make your results unreliable or misleading if not controlled. To choose your variables and controls, you need to identify the causal relationship between the independent and dependent variables, and the potential confounding factors that can interfere with it. You also need to decide how to operationalize your variables, which means how to define and measure them in a consistent and valid way.

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3 Select your sample and design

Your sample is the group of participants or units that you select from your population of interest to conduct your experiment. Your sample should be representative of your population, meaning that it should reflect its characteristics and diversity. To achieve this, you need to use appropriate sampling methods, such as random, stratified, or cluster sampling, depending on your research question and population. You also need to determine your sample size, which is the number of participants or units that you need to obtain sufficient statistical power and accuracy for your experiment. To calculate your sample size, you need to consider your effect size, significance level, and error margin, among other factors. Your design is the way that you assign your sample to different groups or conditions of your experiment. Your design should be suitable for your research question and hypothesis, and it should minimize the sources of bias and error that can affect your results. For example, you can use a randomized, controlled, or factorial design, depending on your variables and controls.

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Natalie Rodrigue

Skilled Biostatistician & Epidemiologist - Mentor, Experienced, Knowledgeable & Passionate Instructor - DoE - Expert in the Analysis of Genomic Data & Statistical Modelling. + 30 yrs experience.
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It is imperative to identify the most important sources of variation (besides that due to experimental factors) and choose a design that has the ability to block for it (randomised complete block design, randomised incomplete block design, cross-over design, repeated measures design, split-plot design, split-block design, using covariates, etc).

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Ron Leavitt, PhD

Expert Scientist at GSK | Assay Development | Next Generation Sequencing (NGS) | Biomarker Scientist | Experimental Design | Bioinformatics | Data Analysis | Molecular Biology
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The sample and design of your experiment are the scaffolding upon which your research stands. Careful consideration must be given to the representativeness and size of your sample, as well as the design that best aligns with your research objectives. Utilize sampling methods that reflect the diversity of your target population, and employ designs that minimize biases and maximize the integrity of your data. A thoughtful approach to sampling and design lays the groundwork for robust and generalizable findings.

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4 Conduct your experiment and collect your data

Once you have planned your experiment, you need to conduct it and collect your data. This involves following your procedures, applying your treatments, measuring your outcomes, and recording your observations. To ensure the quality and integrity of your data, you need to follow some ethical and practical guidelines, such as obtaining informed consent from your participants, protecting their privacy and confidentiality, avoiding coercion or deception, ensuring safety and security, using standardized and calibrated instruments, checking for accuracy and completeness, and storing and backing up your data.

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5 Analyze your data and test your hypothesis

After you have collected your data, you need to analyze it and test your hypothesis. This involves using appropriate statistical methods, such as descriptive, inferential, or exploratory statistics, depending on your research question and hypothesis. You also need to choose the right tests, such as t-tests, ANOVA, or regression, depending on your variables and design. You need to apply the tests correctly, interpret the results accurately, and report the findings clearly. To analyze your data and test your hypothesis, you need to have some basic knowledge of statistics, or consult a statistician if needed.

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Ron Leavitt, PhD

Expert Scientist at GSK | Assay Development | Next Generation Sequencing (NGS) | Biomarker Scientist | Experimental Design | Bioinformatics | Data Analysis | Molecular Biology
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Analysis transforms raw data into meaningful insights, illuminating the underlying patterns and relationships inherent in your experiment. Choose appropriate statistical methods that align with your research design and hypothesis, and apply them rigorously to your data. Robust statistical analysis lends credibility to your findings, enabling you to draw valid conclusions and test the veracity of your hypothesis. Remember, the integrity of your analysis is paramount in establishing the credibility of your research.

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6 Communicate your results and conclusions

The final step of experimental design is to communicate your results and conclusions to your audience, such as your peers, supervisors, or the public. This involves writing a report, a paper, a presentation, or a poster that summarizes your experiment and its outcomes. To communicate your results and conclusions effectively, you need to follow some general and specific guidelines, such as using a clear and concise language, following a logical and coherent structure, citing your sources properly, using appropriate visual aids, such as tables, graphs, or diagrams, highlighting the main points and implications, acknowledging the limitations and future directions, and inviting feedback and questions.

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Ron Leavitt, PhD

Expert Scientist at GSK | Assay Development | Next Generation Sequencing (NGS) | Biomarker Scientist | Experimental Design | Bioinformatics | Data Analysis | Molecular Biology
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Effective communication is the bridge that connects scientific inquiry with broader understanding and application. Articulate your results and conclusions with clarity and precision, conveying the significance of your findings to your intended audience. Craft compelling narratives that contextualize your research within the broader landscape of scientific inquiry, and acknowledge the limitations and implications of your work. By communicating your results transparently and persuasively, you contribute to the collective advancement of knowledge in your field.

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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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Natalie Rodrigue

Skilled Biostatistician & Epidemiologist - Mentor, Experienced, Knowledgeable & Passionate Instructor - DoE - Expert in the Analysis of Genomic Data & Statistical Modelling. + 30 yrs experience.
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In over 30 years experience, I observed that many researchers try to address too many objectives within a single experiment. Experimentation is a sequential process. For instance when exploring a new phenomenon and when several factors are likely to impact the response, one should use a screening design. Then an optimisation design may be used to study further influential factors and carry out with the settings of factor levels. Stay away from one-factor-at-a-time experimentation.

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How do you avoid common pitfalls in experimental design?

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1 Define your research question and hypothesis

2 Choose your variables and controls

3 Select your sample and design

4 Conduct your experiment and collect your data

5 Analyze your data and test your hypothesis

6 Communicate your results and conclusions

7 Here’s what else to consider

Experimental Design

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