Inferential statistics uses analytical tools to draw conclusions about populations from samples. Some common inferential statistics tests include the t-test, which compares means between two groups; correlation coefficients, which measure the strength and direction of relationships between variables; analysis of variance (ANOVA) tests, which compare means across multiple categorical groups; and chi-square tests, which measure how observed data matches a theoretical model. Researchers preset a level of significance, such as 0.01 or 0.05, to determine whether results reject the null hypothesis and are statistically significant.