What type of distribution does parametric tests assume?

Parametric tests assume that the underlying data follows a normal distribution. This is a key assumption for many statistical methods used in psychology research, as parametric tests utilize specific properties related to the mean and standard deviation of the data. When data is normally distributed, it allows for more powerful conclusions to be drawn about the population from which the samples were taken.

Normal distribution implies that the data will have a symmetrical bell-shaped curve, where most of the observations cluster around the central peak and the probabilities for extreme values decrease symmetrically on both sides. This assumption enables parametric tests to provide valid results when making inferences about population parameters based on sample statistics.

In contrast, other types of distributions, such as uniform, skewed, or lognormal distributions, do not meet the criteria required for parametric tests. For instance, a uniform distribution lacks the characteristics of a peak and spreads data evenly across a range, while skewed distributions indicate a concentration of data on one side of the mean. Lognormal distributions imply that the logarithm of the data is normally distributed, which also deviates from the assumptions of standard parametric tests. Thus, the importance of normal distribution in the context of parametric testing is paramount for accurate statistical analysis in psychological research.