This article discusses the importance of checking for normality in statistical analysis, especially for parametric tests that assume normal distribution of data. It provides an overview of methods to assess normality using SPSS, including visual methods and statistical tests. Visual methods such as histograms, stem-and-leaf plots, boxplots, P-P plots, and Q-Q plots are described. These methods help in visually assessing whether data follow a normal distribution. Statistical tests like the Kolmogorov-Smirnov (K-S) test, Lilliefors corrected K-S test, Shapiro-Wilk test, Anderson-Darling test, and others are also discussed. The article emphasizes that while large sample sizes can tolerate deviations from normality, small samples may not. The Shapiro-Wilk test is recommended as the most powerful test for normality, especially for small samples. The article also provides examples of how to test normality using SPSS, including the results for serum magnesium and TSH levels. It concludes that parametric tests should be used for data that is normally distributed, while non-parametric tests should be used for non-normal data. The article also highlights the importance of considering normality when interpreting data from the literature to ensure the correct statistical tests are used.This article discusses the importance of checking for normality in statistical analysis, especially for parametric tests that assume normal distribution of data. It provides an overview of methods to assess normality using SPSS, including visual methods and statistical tests. Visual methods such as histograms, stem-and-leaf plots, boxplots, P-P plots, and Q-Q plots are described. These methods help in visually assessing whether data follow a normal distribution. Statistical tests like the Kolmogorov-Smirnov (K-S) test, Lilliefors corrected K-S test, Shapiro-Wilk test, Anderson-Darling test, and others are also discussed. The article emphasizes that while large sample sizes can tolerate deviations from normality, small samples may not. The Shapiro-Wilk test is recommended as the most powerful test for normality, especially for small samples. The article also provides examples of how to test normality using SPSS, including the results for serum magnesium and TSH levels. It concludes that parametric tests should be used for data that is normally distributed, while non-parametric tests should be used for non-normal data. The article also highlights the importance of considering normality when interpreting data from the literature to ensure the correct statistical tests are used.