In statistics, researchers are often interested in understanding the relationship between one or more explanatory variables and a response variable. However, occasionally there may be…

In statistics, researchers are often interested in understanding the relationship between some independent variable and a dependent variable. However, sometimes an antecedent variable can be present. An…

Aggregation bias occurs when it is wrongly assumed that the trends seen in aggregated data also apply to individual data points. The easiest way to understand…

Often in experimental studies, researchers will have participants provide responses to several different treatments. In these types of studies, order effects refer to differences in participant…

Often in experiments, researchers are interested in understanding the relationship between an explanatory variable and a response variable. Unfortunately nuisance variables often arise in experimental studies,…

In statistics, we fit regression models for two reasons: (1) To explain the relationship between one or more explanatory variables and a response variable. (2) To predict values of a…

A parsimonious model is a model that achieves a desired level of goodness of fit using as few explanatory variables as possible. The reasoning for…

Referral bias is a type of bias that occurs when the types of individuals included in a study are not representative of the individuals in…

A density curve is a curve on a graph that represents the distribution of values in a dataset. It’s useful for three reasons: 1. A density…

A statistical hypothesis is an assumption about a population parameter. For example, we may assume that the mean height of a male in the U.S. is 70 inches.…