This study aims to examine the effects of the underlying population distribution (normal, non-normal) and OLs on the magnitude of Pearson, Spearman and Pearson Winzorized correlation coefficients through Monte Carlo simulation. The study is conducted using Monte Carlo simulation methodology, with sample sizes of 50, 100, 250, 250, 500 and 1000 observations. Each, underlying population correlations of 0.12, 0.20, 0.31 and 0.50 under conditions of bivariate Normality, bivariate Normality with Outliers (discordant, contaminants) and Non-normal with different values of skewness and kurtosis. The results show that outliers have a greater effect compared to the data distributions; specifically, a substantial effect occurs in Pearson and a smaller one in Spearman and Pearson Winzorized. Additionally, the outliers are shown to have an impact on the assessment of bivariate normality using Mardia’s test and problems with decisions based on skewness and kurtosis for univariate normality. Implications of the results obtained are discussed.