Bivariate analysis techniques like correlation, chi-square tests, t-tests, and ANOVA are commonly misused in social science research. A major misuse is assuming causation from correlation between two variables, when there may be confounding factors responsible for their relationship. Researchers may report strong bivariate correlations without investigating alternative explanations. Another mistake is using bivariate analysis on non-random samples, leading to selection bias in findings.

Tests like chi-square and t-tests are sometimes incorrectly applied to non-normally distributed data or small sample sizes. ANOVA is prone to misuse when researchers do not properly check its assumptions like homoscedasticity.

The simplicity of bivariate methods also tempts improper use - for example relying solely on them to understand multifaceted social phenomena. While valuable when applied rigorously, bivariate techniques are open to misapplication in social science.

 Researchers must understand their limitations, avoid drawing unsupported causal inferences, and situate bivariate findings within robust multivariate analysis to produce high-quality social science.