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# Parametric and Non-Parametric Tests In research methodology and statistical analysis, statistical tests are used to examine research hypotheses, determine relationships between variables, compare groups, and draw conclusions from sample data. Broadly, statistical tests can be classified into parametric and non-parametric tests. The choice between these two types depends mainly on the nature of the data, the assumptions made about the population distribution, the measurement scale, and the purpose of the research. A proper understanding of parametric and non-parametric tests is essential because the inappropriate selection of a statistical test may lead to incorrect or misleading results. Parametric tests are statistical procedures that make certain assumptions about the population from which the sample is drawn. The most important assumption is generally that the data follow a specific probability distribution, particularly the normal distribution. Parametric tests are usually applied to quantitative data measured on interval or ratio scales. They are particularly useful when the sample is reasonably large and the assumptions regarding normality, independence, and homogeneity of variance are satisfied. Since these tests use population parameters such as the mean and standard deviation, they are called parametric tests. Common examples of parametric tests include the z-test, t-test, analysis of variance (ANOVA), Pearson's correlation coefficient, and regression analysis. The z-test is generally used to determine whether there is a significant difference between a sample statistic and a population parameter or between two sample means when the population variance is known or the sample size is sufficiently large. The t-test is commonly used when comparing means, especially when the sample size is relatively small and the population standard deviation is unknown. The t-test has several forms. An independent-samples t-test is used ___ ____ _____ ___ ____ _________ ________ ____ _______ _____ _______.
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