How do you formulate and test hypotheses based on your exploratory data analysis?

2 Why do you need hypotheses?

Hypotheses help you focus your EDA on specific questions and goals, and provide a framework for interpreting and communicating your findings. By formulating hypotheses, you can narrow down the scope of your data exploration, and identify the most relevant variables, metrics, and methods to use. By testing hypotheses, you can validate or reject your assumptions, and discover new insights and relationships in your data. Hypotheses also help you bridge the gap between EDA and inferential statistics or machine learning, as they can serve as the basis for further analysis and modeling.

3 How do you formulate hypotheses?

There is no single formula for generating hypotheses, but there are some general guidelines and sources that can help you. First, you should have a clear understanding of the context and purpose of your data analysis, and the problem or question you want to solve or answer. Second, you should review the existing literature, theories, or best practices related to your domain or topic, and see if there are any established hypotheses or gaps that you can build on or fill. Third, you should conduct some preliminary EDA, such as descriptive statistics, data cleaning, and exploratory plots, to get a sense of the distribution, variability, and correlation of your data. Fourth, you should use your creativity, curiosity, and logic to generate hypotheses that are specific, measurable, and testable.

4 How do you test hypotheses?

Once you have formulated your hypotheses, you need to test them using appropriate tools and techniques. There are two main types of hypothesis testing: visual and statistical. Visual hypothesis testing involves creating and comparing graphs or charts that illustrate your hypotheses, such as scatter plots, box plots, bar charts, or line charts. Visual hypothesis testing can help you quickly and intuitively assess the validity of your hypotheses, and identify outliers, trends, or anomalies in your data. Statistical hypothesis testing involves applying mathematical formulas and tests that quantify the strength of the evidence for or against your hypotheses, such as t-tests, ANOVA, chi-square, or regression. Statistical hypothesis testing can help you measure the significance, effect size, and confidence level of your hypotheses, and control for confounding factors or errors in your data.

6 How do you communicate and use your hypothesis testing results?

The final step of hypothesis testing is to communicate and use your results to inform your further analysis and decision making. To communicate your results effectively, you should summarize and visualize the main findings and outcomes of your tests, and explain how they relate to your hypotheses and goals. You should also report the limitations and uncertainties of your tests, and acknowledge any alternative or conflicting hypotheses or evidence. To use your results productively, you should evaluate and update your hypotheses and assumptions based on your tests, and identify any new questions or hypotheses that emerge from your tests. You should also consider how your results can inform your next steps, such as inferential statistics, machine learning, or action plans.