What is the effect of violating the assumptions of parametric tests?

Violating the assumptions of parametric tests can indeed lead to incorrect conclusions. Parametric tests, like t-tests or ANOVAs, rely on specific assumptions such as normality of the data, homogeneity of variance, and independence of observations. When these assumptions are not met, the results of the test can become unreliable. For example, if the data are not normally distributed, it could result in inaccurate p-values, leading researchers to make false rejections or failures to reject the null hypothesis. Consequently, interpretations based on such results may not reflect the true characteristics of the data or the population from which the sample was drawn. Therefore, understanding and adhering to the assumptions of parametric tests is crucial for ensuring the validity of statistical conclusions.