Pearson's r assumes that the relationship between the two variables is linear, the data for each variable is normally distributed, and the data is measured at the interval or ratio level. Violating these assumptions can lead to inaccurate correlation estimates, making it crucial to assess them before applying Pearson's r in analyses.