Pearson's correlation is important to use when two variables are continous and have normal distribution. It is the most commonly used statistic to measure the degree of relationship between linearly related variables. The Pearson correlation coefficient ranges from -1 to +1 a value less than 0 indicates that there is a negative correlation, i.e. they are inversely associated, and a value greater than 0 indicates that there is a positive correlation, i.e. they would be directly associated. It requieres assumptions such as intervals/reason level data and normality. Spearman's rho or Spearman's rank correlation coefficient measures the strength and direction of the association between two ranked variables, it is used when the dsta are ordinal or when assumptions of normality are not met. Spearman doesn't requiere normality because it uses ranks and is less sensitive to outliers than Pearson. Finally the chi-square test helps us to evaluate the differences between the observed values and the expected values in one or more categories, it examines whether there is a significant association between them. It doesn't requiere normality, but it does requiere a sufficient sample size and adequate expected frecuencies for the results to be valid. Using the wrong test can lead to erroneous conclusions about the relationship between variables, which is why it is important to understand the level of measurement and the distribution of the data.