Hypothesis testing is a statistical technique used in data analytics to determine whether a statement about a population is supported by sample data. The process begins by formulating two competing hypotheses: the null hypothesis (denoted as (H_0)), which represents the default or no-effect scenario, and the alternative hypothesis (denoted as (H_1)), which represents the research question or effect we wish to prove. For example, if a developer wants to know if a new app feature improves user engagement compared to the current version, the null hypothesis could state that there's no difference in engagement between the two versions, while the alternative hypothesis might claim that the new feature increases engagement.
Once the hypotheses are established, the next step is to collect sample data and perform a statistical test to analyze it. Common tests include t-tests, chi-square tests, and ANOVA, depending on the nature of the data and the specific question being addressed. By applying these tests, developers can calculate a test statistic and a corresponding p-value, which indicates the probability of observing the sample data—or something more extreme—under the assumption that the null hypothesis is true. For instance, if the p-value obtained is 0.03, this suggests that there is a 3% probability that the observed data occurred by random chance alone if the null hypothesis holds.
The final stage in hypothesis testing is to make a decision based on the p-value and a pre-defined significance level (typically set at 0.05). If the p-value is less than the significance level, the null hypothesis is rejected, implying that there is enough evidence to support the alternative hypothesis. In our earlier example, if the p-value is 0.03, the developer may conclude that the new feature does significantly improve user engagement. Conversely, if the p-value is greater than 0.05, it would indicate that there is insufficient evidence to reject the null hypothesis, leading to the conclusion that the new feature does not provide a notable advantage over the existing version. This structured approach allows developers and analysts to make data-driven decisions based on statistical evidence.