How do you share hypothesis testing results with other data analysts?

1 Know your audience

Before you present your results, you need to understand who your audience is and what they expect from your analysis. Are they familiar with the topic and the data source? Do they have a specific question or problem that they want to solve? Do they need a high-level overview or a detailed explanation? Knowing your audience will help you tailor your message, tone, and format to suit their needs and preferences.

2 Define your hypothesis and assumptions

One of the first steps in sharing your results is to define your hypothesis and the assumptions that you made in your analysis. A hypothesis is a statement that expresses a relationship between two or more variables that you want to test. For example, you may want to test if there is a difference in the average sales between two groups of customers. An assumption is a condition that you assume to be true for your analysis to be valid. For example, you may assume that the samples are independent, normally distributed, and have equal variances. Defining your hypothesis and assumptions will help you set the context and the scope of your analysis and avoid confusion or misunderstanding.

3 Choose the appropriate test and significance level

Another step in sharing your results is to choose the appropriate test and significance level for your hypothesis. A test is a statistical procedure that compares the observed data with the expected data under the null hypothesis, which is the opposite of your hypothesis. For example, if your hypothesis is that the average sales are different between two groups, the null hypothesis is that they are equal. A significance level is a threshold that determines how likely it is that the observed difference is due to chance or sampling error. For example, a significance level of 0.05 means that you are willing to accept a 5% chance of rejecting the null hypothesis when it is true. Choosing the appropriate test and significance level will help you justify your method and criteria for your analysis.

4 Report the test statistic and p-value

The next step in sharing your results is to report the test statistic and the p-value for your test. A test statistic is a numerical value that summarizes the evidence from the sample against the null hypothesis. For example, a t-test statistic measures the difference between the sample means relative to the sample standard deviations. A p-value is the probability of obtaining a test statistic as extreme or more extreme than the one observed, assuming that the null hypothesis is true. For example, a p-value of 0.01 means that there is a 1% chance of observing such a difference or greater if the null hypothesis is true. Reporting the test statistic and the p-value will help you quantify and communicate the strength and direction of your evidence.

5 Interpret the results and draw conclusions

The final step in sharing your results is to interpret the results and draw conclusions based on your hypothesis and significance level. To interpret the results, you need to compare the p-value with the significance level and decide whether to reject or fail to reject the null hypothesis. For example, if the p-value is less than the significance level, you can reject the null hypothesis and conclude that there is a statistically significant difference between the groups. To draw conclusions, you need to relate the results to the context and the purpose of your analysis and explain what they mean in practical terms. For example, you can say that the average sales of group A are higher than group B by $10 with a 95% confidence level. Interpreting the results and drawing conclusions will help you answer your research question and provide recommendations or implications for action.

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?