How can you apply hypothesis testing in your industry?

1 What is hypothesis testing?

Hypothesis testing is a statistical method that enables you to assess a claim or assumption about a population parameter, such as a mean, a proportion, or a correlation. For instance, you might want to evaluate whether the average satisfaction rating of your customers is higher than 4 out of 5, or if the proportion of male and female users of your app is equal, or if there is a positive relationship between the number of features and the retention rate of your product. To perform a hypothesis test, you need to follow these steps: define a null hypothesis and an alternative hypothesis; choose a significance level; collect and analyze the data; determine the p-value; and compare the p-value to the significance level and make a decision. The null hypothesis is the default or status quo assumption that you want to challenge, while the alternative hypothesis is its opposite. The significance level is usually denoted by alpha and set at 0.05 or 5%, which implies that you are willing to accept a 5% chance of making a false positive conclusion. The test statistic measures how far the sample data deviates from the null hypothesis. The p-value indicates how likely or unlikely the data is under the null hypothesis. If the p-value is less than or equal to the significance level, you can reject the null hypothesis and accept the alternative one; otherwise, you cannot reject it and have to accept it as a possible explanation.

2 How to choose the right test?

When selecting a hypothesis test to use in different situations and data types, it is important to consider the type and scale of the data, the number and nature of samples, and the type and direction of the hypothesis. For example, a one-sample t-test can be used to compare the mean of a single sample to a known or hypothesized value, such as when checking if the average age of customers is different from 30. A two-sample t-test can compare the means of two independent samples from two populations, while a paired t-test can compare the means of two dependent or paired samples from the same population. Similarly, a one-sample proportion test can compare the proportion of a single sample to a known or hypothesized value, while a two-sample proportion test can compare the proportions of two independent samples from two populations. Additionally, a chi-square test can be used to compare observed frequencies of categorical data to expected frequencies under a certain distribution. Furthermore, correlation tests measure the strength and direction of linear relationships between numerical variables. Lastly, regression tests model relationships between one or more independent variables and a dependent variable. To choose the right test for your situation, you may use tables or flowcharts that show common tests for different data types and hypotheses.

3 How to interpret and communicate the results?

Once you have performed the hypothesis test and obtained the p-value and the test statistic, you need to interpret and communicate the results in a clear and concise way. To do this, start by stating the null and alternative hypotheses in plain language, for example, "We want to test if the average rating of our customers is higher than 4." Then, state the significance level and the p-value numerically, such as "We set the significance level at 0.05 and obtained a p-value of 0.01." Next, state the decision and conclusion logically, like "Since the p-value is less than the significance level, we reject the null hypothesis and conclude that the average rating of our customers is higher than 4." Additionally, provide the test statistic and its meaning in contextual terms, for instance "We used a one-sample t-test and obtained a test statistic of 2.5, which means that the sample mean is 2.5 standard deviations away from the hypothesized mean of 4." Furthermore, state the confidence interval and its interpretation in practical terms; for example, "We calculated a 95% confidence interval for the sample mean, which is (4.2, 4.8). This means that we are 95% confident that the true population mean is between 4.2 and 4.8." Additionally, discuss any limitations or assumptions of the test or data critically; such as "We assume that the sample is representative of the population, that the data is normally distributed, and that there are no outliers or missing values. These assumptions may affect the validity and reliability of the results." Finally, state any implications or recommendations of your results actionably; like "The results suggest that our customers are satisfied with our product and that we can increase our prices, improve our features, or expand our market. However, we should also conduct more surveys, interviews, or experiments to understand the factors that influence customer satisfaction and loyalty."

4 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?