Heuristics are quick rules-of-thumb and easy-to-use tools that people often use to make decisions. Heuristics have been identified by research psychologists to describe and explain behavior.

With the representative heuristic, people will estimate the likelihood or probability of an event based on how similar it is to other known situations. With the heuristic of ignoring base rates, people will ignore hard data and will instead estimate probabilities based on narratives, even if the narratives are lacking in relevant details. With the availability heuristic, people estimate likelihood based on how quickly and easily something comes to mind. With anchoring and adjustment, people will first anchor an initial estimate based on the first number indicated, and they will adjust their estimate up or down from there. With satisficing, people will look for an acceptable solution that meets minimum requirements, but will not invest additinoal resources in seeking a perfect or optimum solution. Regression toward the mean explains why initial outliers move closer to the average with subsequent repetition.

A correlation coefficient can be used to make predictions of dependent variable values using a procedure called linear regression. There are two equations that can be used to perform regression: the standardized regression equation and the unstandardized regression equation. Both regression equations produce a straight line that represents the predicted value on the dependent variable for a sample member with a given X variable score.

One statistical phenomenon to be aware of when making predictions is regression towards the mean, which occurs when a predicted dependent variable value that is closer to the mean of the dependent variable than the person’s score on the independent variable was to the mean of the independent variable. This means that outliers and rare events can be difficult or impossible to predict via the regression equations.

There are important assumptions of Pearson’s r and regression: (1) a linear relationship between variables, (2) homogeneity of residuals, (3) an absence of a restriction of range, (4) a lack of outliers/extreme values that distort the relationship between variables, (5) subgroups within the sample are equivalent, and (6) interval- or ratio-level data for both variables. Violating any of these assumptions can distort the correlation coefficient.