Hypothesis testing is a fundamental statistical method used to make inferences about a population based on sample data. It involves formulating two competing hypotheses: the null hypothesis (H₀), which assumes no effect or difference, and the alternative hypothesis (H₁), which suggests that an effect or difference exists. By analyzing sample data, hypothesis testing helps determine whether observed effects are statistically significant or could have occurred by random chance. Key components of hypothesis testing include the significance level (α), p-value, and test statistics, which guide researchers in making informed decisions based on their data.