Hypothesis testing is a statistical method used to make inferences about a population based on sample data. It allows researchers to determine whether an observed effect is statistically significant or if it could have occurred by random chance. This process is essential in various fields, including data science, where it helps in decision-making based on experimental data.

The key concepts in hypothesis testing include the null hypothesis (H₀) and the alternative hypothesis (H₁ or Ha). The null hypothesis represents the default assumption that no effect or difference exists, while the alternative hypothesis suggests that an effect or difference does exist. A significance level (α), often set at 0.05, is used to determine the threshold for rejecting the null hypothesis. The p-value is another critical component, indicating the probability of observing the data if the null hypothesis is true. A small p-value (≤ α) suggests strong evidence against H₀, leading to its rejection ²³.

In summary, hypothesis testing is a fundamental tool for validating assumptions and making informed decisions based on data.