Hypothesis testing is a statistical method used to make inferences about a population based on sample data. It helps determine whether an observed effect is statistically significant or if it occurred by random chance. The process involves formulating two competing hypotheses: the null hypothesis (H₀), which assumes no effect or difference exists, and the alternative hypothesis (H₁ or Ha), which suggests that an effect or difference does exist ².

Key components of hypothesis testing include the significance level (α), commonly set at 0.05, which is the probability threshold for rejecting the null hypothesis. The p-value is another crucial element; it measures the probability that the observed data would occur if the null hypothesis were true. A small p-value (≤ α) indicates strong evidence against H₀, leading to its rejection ²³.

Test statistics, such as the Z-score and T-score, are calculated from sample data to help determine whether to reject the null hypothesis. These statistics are essential for making informed decisions based on experimental data ²⁴.