What Are Parametric and Nonparametric Tests?

This is a basic calculator and a statistical table.
••• Creatas/Creatas/Getty Images

In statistics, parametric and nonparametric methodologies refer to those in which a set of data has a normal vs. a non-normal distribution, respectively. Parametric tests make certain assumptions about a data set; namely, that the data are drawn from a population with a specific (normal) distribution. Non-parametric tests make fewer assumptions about the data set. The majority of elementary statistical methods are parametric, and parametric tests generally have higher statistical power. If the necessary assumptions cannot be made about a data set, non-parametric tests can be used. Here, you will be introduced to two parametric and two non-parametric statistical tests.

Parametric Test for Independent Measures Between Two Groups: t-test

This is a girl learning to add.
••• Brand X Pictures/Brand X Pictures/Getty Images

A t-test is used to compare between the means of two data sets, when the data is normally distributed. The two groups of data must be independent from one another. The t statistic is equal to the difference between the group means divided by the standard error of the difference between the group means.

Parametric Correlation Test: Pearson

This is a graph displaying statistical data.
••• Thinkstock Images/Comstock/Getty Images

A common parametric method of measuring correlation between two variables is the Pearson Product-Moment Correlation. The two variables, x and y, must each be normally distributed. The means and variances of the variables is calculated. Then, the correlation can be calculated as the covariance between the two variables divided by the product of their standard deviations.

Non-Parametric Correlation Test: Spearman

This is a man analyzing statistical data.
••• Goodshoot/Goodshoot/Getty Images

The Spearman Rank Correlation Coefficient is similar to the Pearson coefficient, but is used when data are ordinal (usually categorical data, set into a position on some kind of scale) rather than interval (data measured along a scale where all data points are equidistant from one another). This test essentially works the same way as the Pearson Correlation test, only the data must first be ranked.

Non-Parametric Test for Independent Measures Between Two groups: Mann-Whitney test

There are many types of data, and thus many different statistical methods.
••• John Foxx/Stockbyte/Getty Images

The Mann-Whitney Test is used to compare the means between two groups of ordinal (thus, non-parametric) data. The Mann-Whitney statistic (U) is calculated by putting all the data (scores) into rank order. Then, U is the sum of the numbers of scores from the experimental group that are less than each of a control group.

Related Articles

Can You Use a T-Test on Ranked Data?
How to Interpret an Independent T Test in SPSS
What Does a Negative T-Value Mean?
How to Calculate Stanine Scores
What Statistical Analysis Do I Run When Comparing Three...
How to Convert ASVAB Scores
How to Find a Z Score
How to Calculate ANOVA by Hand
What Is the Tukey HSD Test?
How to Calculate a T-Score
How to Calculate Statistical Significance
How to Calculate the Grand Mean
How to Calculate the Interquartile Range
The Advantages of Using an Independent Group T-Test
How to Calculate the Coefficient of Variation
How to Calculate a Sigma Value
The Relationship Between Standard Deviations & Percentiles
Statistical Analysis Tools
How to Know if Something Is Significant Using SPSS
How to Calculate F-Values

Dont Go!

We Have More Great Sciencing Articles!