This article, authored by Asghar Ghasemi and Saleh Zahediasl, provides a guide for non-statisticians on checking for normality in statistical analysis using SPSS. The authors emphasize the importance of verifying the normality assumption for parametric tests, which are widely used in scientific literature. They highlight that about 50% of published articles contain at least one statistical error, and many procedures, such as correlation, regression, t-tests, and ANOVA, assume normal data distribution. The article discusses both visual methods and statistical tests for assessing normality. Visual methods include frequency distributions, stem-and-leaf plots, boxplots, P-P plots, and Q-Q plots. These methods help visually inspect the data distribution but are often unreliable. Statistical tests, such as the Kolmogorov-Smirnov (KS) test, Lilliefors corrected KS test, Shapiro-Wilk test, Anderson-Darling test, Cramer-von Mises test, D'Agostino skewness test, Ancombe-Glynn kurtosis test, D'Agostino-Pearson omnibus test, and the Jarque-Bera test, are more reliable for assessing normality. The Shapiro-Wilk test is particularly recommended due to its higher power compared to other tests. The authors use examples from serum magnesium levels and serum thyroid-stimulating hormone (TSH) levels to illustrate how to apply these tests in SPSS. They conclude that while visual methods can provide initial insights, statistical tests are essential for accurate and reliable conclusions. The Shapiro-Wilk test is highly recommended for its effectiveness in detecting non-normal distributions, especially in large samples. The article emphasizes the importance of considering normality assumptions to ensure the validity of parametric statistical tests and to validate data presented in scientific literature.This article, authored by Asghar Ghasemi and Saleh Zahediasl, provides a guide for non-statisticians on checking for normality in statistical analysis using SPSS. The authors emphasize the importance of verifying the normality assumption for parametric tests, which are widely used in scientific literature. They highlight that about 50% of published articles contain at least one statistical error, and many procedures, such as correlation, regression, t-tests, and ANOVA, assume normal data distribution. The article discusses both visual methods and statistical tests for assessing normality. Visual methods include frequency distributions, stem-and-leaf plots, boxplots, P-P plots, and Q-Q plots. These methods help visually inspect the data distribution but are often unreliable. Statistical tests, such as the Kolmogorov-Smirnov (KS) test, Lilliefors corrected KS test, Shapiro-Wilk test, Anderson-Darling test, Cramer-von Mises test, D'Agostino skewness test, Ancombe-Glynn kurtosis test, D'Agostino-Pearson omnibus test, and the Jarque-Bera test, are more reliable for assessing normality. The Shapiro-Wilk test is particularly recommended due to its higher power compared to other tests. The authors use examples from serum magnesium levels and serum thyroid-stimulating hormone (TSH) levels to illustrate how to apply these tests in SPSS. They conclude that while visual methods can provide initial insights, statistical tests are essential for accurate and reliable conclusions. The Shapiro-Wilk test is highly recommended for its effectiveness in detecting non-normal distributions, especially in large samples. The article emphasizes the importance of considering normality assumptions to ensure the validity of parametric statistical tests and to validate data presented in scientific literature.