Hypothesis testing is a fundamental technique in data analysis used to determine whether there is enough statistical evidence to support a specific hypothesis about a population parameter. It involves formulating two competing hypotheses: the null hypothesis (H0​), which represents the status quo or a statement of no effect, and the alternative hypothesis (H1​ or Ha​), which represents the presence of an effect or a difference. The process starts with selecting an appropriate statistical test based on the data type and research question. Next, a significance level (α), often set at 0.05, is chosen to determine the threshold for rejecting the null hypothesis. Data is then analyzed, and a test statistic is calculated. This statistic is compared to a critical value derived from statistical tables or p-values to assess whether the observed results are unlikely under the null hypothesis. If the test statistic exceeds the critical value or the p-value is below the significance level, the null hypothesis is rejected in favor of the alternative hypothesis. Hypothesis testing helps in making informed decisions by quantifying the evidence against the null hypothesis, guiding conclusions and further research.