# PARAMETRIC TESTS IN STATISTICS PDF

It's safe to say that most people who use statistics are more familiar with parametric analyses than nonparametric analyses. Nonparametric tests. Parametric tests. 1. Parametric Tests Dr Heena Sharma 1; 2. Contents • Introduction to statistical tests • System for statistical analysis. Typically, people who perform statistical hypothesis tests are more comfortable with parametric tests than nonparametric tests. You've probably. Author: Justus Runte Country: Gambia Language: English Genre: Education Published: 6 January 2016 Pages: 467 PDF File Size: 10.15 Mb ePub File Size: 42.91 Mb ISBN: 957-4-33056-907-5 Downloads: 24312 Price: Free Uploader: Justus Runte

PARAMETRIC TESTS IN STATISTICS PDF

If you'd rather get 1: We don't parametric tests in statistics to assume anything about the distribution of test scores to reason that before we gave the test it was equally likely that the highest score would be any of the first Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn.

This is often the assumption that the population data are normally distributed. Table 3 shows the non-parametric equivalent of a number of parametric tests. Table 3 Parametric and Non-parametric tests for comparing two or more groups Non-parametric tests are valid for both non-Normally distributed data and Parametric tests in statistics distributed data, so why not use them all the time?

It would seem prudent to use non-parametric tests in all cases, which would save one the bother of testing for Normality.

## Nonparametric Tests vs. Parametric Tests - Statistics By Jim

Parametric tests are preferred, however, for the following reasons: We are rarely interested in a significance test alone; we would like to say something about the parametric tests in statistics from which the samples came, and this is best done with estimates of parameters and confidence intervals.

It is difficult to do flexible modelling with non-parametric tests, for example allowing for confounding factors using multiple regression.

Parametric tests usually have more statistical power than their non-parametric equivalents. In other words, one is more likely to detect significant differences when they truly exist. Let me explain using the distribution of salaries. Salaries tend to be a right-skewed distribution. The majority of wages cluster around the median, which is the point where half are above and half are below. However, there is a long tail parametric tests in statistics stretches into the higher salary ranges.

### Parametric and Non-parametric tests for comparing two or more groups

This long tail pulls the mean far away from the central median value. The two distributions are typical for salary distributions. These two distributions have roughly equal medians but parametric tests in statistics means. A statistical test used in the case of non-metric independent variables is called nonparametric test.

### Parametric statistics

In the parametric test, the test statistic is based on distribution. On the other hand, the test statistic is arbitrary in the case of the nonparametric test. In the parametric test, it is assumed that the measurement of variables of interest is done on interval or ratio level.