Inferential statistics suggest statements about a population based on a sample from that population. Parametric inferential tests are carried out on data that follow certain parameters: the data will be normal (i.e. the distribution parallels the bell curve); numbers can be added, subtracted, multiplied and divided; variances are equal when comparing two or more groups; and the sample should be large and randomly selected.
There are generally more statistical technique options for the analysis of parametric than non-parametric data, and parametric statistics are considered to be the more powerful. Common examples of parametric tests are: correlated t-tests and the Pearson r correlation coefficient.
- Parametric vs. Non-parametric tests. This web page provides a table which demonstrates the various differences between parametric and non-parametric tests
Woolf, L. M. (n.d.). Introduction to measurement and statistics. Retrieved from http://www.webster.edu/~woolflm/statwhatis.html
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