How can you validate your data analytics hypotheses and assumptions through experiments?

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Data analytics is the process of transforming data into insights that can help you make better decisions, optimize processes, or create new value. But how do you know if your data analytics hypotheses and assumptions are valid and reliable? How can you test them before investing too much time, money, or resources in a project? One way to do that is through experiments, which are systematic and controlled methods of testing your hypotheses and assumptions against real data and outcomes. In this article, you will learn how to design, run, and analyze experiments for data analytics projects, and what tools and techniques you can use to make them more effective and efficient.

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Errin Samuelsz

Sr. Scientist Bioinformatics at Cardiff Oncology

1 Define your problem and hypotheses

Before you can run any experiment, you need to have a clear and specific problem statement that defines what you want to achieve, why it matters, and how you will measure success. For example, you might want to increase customer retention, reduce operational costs, or improve product quality. Next, you need to formulate your hypotheses, which are tentative explanations or predictions about how your problem can be solved or improved. For example, you might hypothesize that offering a discount, changing a feature, or segmenting your audience will have a positive impact on your problem. Your hypotheses should be based on data, logic, and domain knowledge, and they should be testable and falsifiable.

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Errin Samuelsz

Sr. Scientist Bioinformatics at Cardiff Oncology
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One helpful exercise in hypothesis generation is pre-interpreting possible results. For example, if your hypothesis turns out to be true, what would be your next step? Similarly, if your hypothesis is rejected, do you have the information you need to formulate your next hypothesis? Would your current hypothesis provide an interesting insight, or does it actually get you closer to your goal?

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2 Design your experiment

Once you have your problem and hypotheses, you need to design your experiment to test them. This involves choosing your variables, your data sources, your sample size, your experimental design, and your statistical methods. Your variables are the factors that you manipulate (independent variables) or measure (dependent variables) in your experiment. For example, if you want to test the effect of a discount on customer retention, your independent variable is the discount amount, and your dependent variable is the retention rate. Your data sources are the places where you collect or access your data, such as databases, surveys, web analytics, or sensors. Your sample size is the number of observations or units that you include in your experiment, which should be large enough to ensure validity and reliability, but not too large to waste resources or time. Your experimental design is the way you assign your units to different conditions or groups, such as randomization, matching, or blocking. Your statistical methods are the techniques you use to analyze your data and draw conclusions, such as hypothesis testing, confidence intervals, or regression analysis.

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Errin Samuelsz

Sr. Scientist Bioinformatics at Cardiff Oncology
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Keeping track of your variables is essential, as is keeping track of your hypothesis. What I mean by this, is that sometimes it can be tempting to add additional conditions, sample sets etc… when designing your experiment, only to realize during analysis that now you have too few replicates in one group, or too many variables in another, or just that your experiment is invalid because it became too complex and a mistake was made. Keep the scope of the experiment in focus, and the variables to a minimum. Often, it can be more efficient to perform a series of well-designed, fully thought-out simple experiments, rather than have to repeat a complex experiment multiple times.

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3 Run your experiment

After you have designed your experiment, you need to run it according to your plan and collect your data. This involves setting up your data collection tools, ensuring data quality and integrity, and monitoring your experiment progress and performance. Your data collection tools are the instruments or platforms that you use to capture or store your data, such as forms, scripts, dashboards, or APIs. You should make sure that your data collection tools are reliable, accurate, and consistent, and that they follow ethical and legal standards. You should also check your data quality and integrity regularly, and address any issues or errors that might affect your results, such as missing values, outliers, or biases. You should monitor your experiment progress and performance to see if your experiment is running as expected, and if you need to make any adjustments or stop it early, such as changing your sample size, your variables, or your criteria.

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4 Analyze your results

Once you have run your experiment and collected your data, you need to analyze your results and see if they support or reject your hypotheses. This involves cleaning, exploring, and modeling your data, and interpreting and communicating your findings. Cleaning your data means preparing it for analysis by removing or correcting any errors, inconsistencies, or redundancies. Exploring your data means summarizing and visualizing it to get a general overview and identify any patterns, trends, or anomalies. Modeling your data means applying your statistical methods to test your hypotheses and estimate your effects. Interpreting your findings means understanding what your results mean and how they relate to your problem and hypotheses. Communicating your findings means presenting and reporting your results to your stakeholders or audience, using clear, concise, and compelling language and visuals.

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Errin Samuelsz

Sr. Scientist Bioinformatics at Cardiff Oncology
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Ive found it helpful to compartmentalize analysis and interpretation of the results. Present the data and its relation to the hypotheses — did I prove or reject my hypotheses, and is there a way to visualize this (the answer is usually yes!)? Now that I have answer(s) to my hypothesis… how does this relate to my original problem? Just as important, what do these data not answer? Remember that the experiment was designed to answer specific hypotheses… try to refrain from making interpretations that fall outside of the scope of what was actually being tested.

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5 Learn and iterate

The final step in validating your data analytics hypotheses and assumptions through experiments is to learn from your results and iterate on your process. This means evaluating your experiment quality and outcomes, and identifying any gaps, limitations, or opportunities for improvement. Evaluating your experiment quality and outcomes means assessing how well your experiment was designed, run, and analyzed, and how confident and relevant your results are. You should consider factors such as validity, reliability, generalizability, and causality, and compare your results with other sources of evidence or benchmarks. Identifying any gaps, limitations, or opportunities for improvement means recognizing any areas where your experiment could be enhanced, refined, or extended, and how you can apply your learnings to your next steps or actions. You should consider factors such as feedback, assumptions, risks, and impact, and prioritize your recommendations or suggestions.

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6 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 can you validate your data analytics hypotheses and assumptions through experiments?

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1 Define your problem and hypotheses

2 Design your experiment

3 Run your experiment

4 Analyze your results

5 Learn and iterate

6 Here’s what else to consider

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