rewrite for better flow and simplified: When we run a study, whether it's correlational or an experiment, we learn several things about observed relationships between variables. First, we determine whether they are statistically significant. Without going into too many details, if something is statistically significant, that means that a relationship between two variables in a correlational study, or a difference between two variables in an experiment, is substantial enough that there's less than a 5% chance that it could have occurred by chance, which this is indicated by a p value, which is the probability of obtaining a result if there is no difference between groups or no relationship between variables. The norm in psychology is that if the p-value is smaller than 0.05, then we consider it statistically significant. In addition to learning about the relationship or difference, we can also learn something about its magnitude, which we refer to as an effect size. In correlational studies, the effect size is represented by the correlation, denoted by the letter R, which ranges from -1 to 1, with 0 indicating no relationship between the two variables. In a simple experiment, the effect size is based on the difference between the average scores in each of the two conditions, for which we often use a measure called Cohen's D