1 Define your objectives

Before you start any hypothesis test, you should clearly define your objectives. What are you trying to learn from the data? What are the research questions or hypotheses that you want to test? How will you measure the outcomes and compare the groups? How will you interpret and communicate the results? Defining your objectives will help you choose the appropriate test, design, and sample size for your analysis.

2 Review your data

Once you have defined your objectives, you should review your data to make sure it meets the assumptions and requirements of your chosen test. For example, if you are using a t-test to compare the means of two groups, you should check if the data is normally distributed, independent, and homoscedastic. You should also look for any outliers, missing values, or errors that may affect your analysis. Reviewing your data will help you identify any potential problems or limitations that may require you to refine your hypothesis test.

3 Adjust your test

If you find that your data does not meet the assumptions or requirements of your chosen test, you may need to adjust your test accordingly. For example, if your data is not normally distributed, you may need to use a nonparametric test, such as a Mann-Whitney U test, instead of a t-test. If your data has outliers, you may need to use a robust test, such as a trimmed mean t-test, instead of a standard test. If your data has missing values, you may need to use an imputation method, such as mean or median imputation, to fill in the gaps. Adjusting your test will help you avoid bias and increase the accuracy of your results.

4 Evaluate your results

After you have adjusted your test, you should evaluate your results to see if they answer your objectives. You should look at the test statistic, the p-value, and the effect size to determine if there is a significant difference between the groups and how large it is. You should also look at the confidence interval to estimate the range of values that are likely to contain the true difference. Evaluating your results will help you draw conclusions and make recommendations based on your analysis.

5 Update your objectives

Sometimes, you may need to update your objectives based on your results or new information. For example, if your results are inconclusive or unexpected, you may need to revise your research questions or hypotheses to reflect the new findings. If you receive feedback or suggestions from your stakeholders or peers, you may need to incorporate them into your objectives to address their needs or interests. If you discover new data or insights, you may need to add or modify your objectives to explore them further. Updating your objectives will help you refine your hypothesis test and make it more relevant and useful.