Hypothesis Testing is a method of statistical inference used to decide whether the data at hand sufficiently supports a particular hypothesis. This process involves formulating a null hypothesis (H0), which represents no effect or status quo, and an alternative hypothesis (H1), which represents a significant effect or difference. Statistical tests, such as t-tests, chi-square tests, and ANOVA, are used to calculate a p-value, which indicates the probability of observing the data if the null hypothesis is true. If the p-value is below a predetermined threshold (usually 0.05), the null hypothesis is rejected in favor of the alternative hypothesis. Hypothesis testing helps researchers and analysts make data-driven decisions by evaluating the validity of assumptions and claims based on sample data. It is widely used in scientific research, business analytics, and quality control.