File Name: parametric methods and their non parametric counter parts.zip
Topics: Hypothesis Testing , Statistics. That sounds like a nice and straightforward way to choose, but there are additional considerations. Nonparametric tests are like a parallel universe to parametric tests.
Need a hand? All the help you want just a few clicks away. Therefore, several conditions of validity must be met so that the result of a parametric test is reliable. They can thus be applied even if parametric conditions of validity are not met. Parametric tests often have nonparametric equivalents.
Metrics details. It has generally been argued that parametric statistics should not be applied to data with non-normal distributions. Empirical research has demonstrated that Mann-Whitney generally has greater power than the t -test unless data are sampled from the normal. In the case of randomized trials, we are typically interested in how an endpoint, such as blood pressure or pain, changes following treatment. The objectives of this study were: a to compare the relative power of Mann-Whitney and ANCOVA; b to determine whether ANCOVA provides an unbiased estimate for the difference between groups; c to investigate the distribution of change scores between repeat assessments of a non-normally distributed variable. Polynomials were developed to simulate five archetypal non-normal distributions for baseline and post-treatment scores in a randomized trial. Simulation studies compared the power of Mann-Whitney and ANCOVA for analyzing each distribution, varying sample size, correlation and type of treatment effect ratio or shift.
The three modules on hypothesis testing presented a number of tests of hypothesis for continuous, dichotomous and discrete outcomes. Tests for continuous outcomes focused on comparing means, while tests for dichotomous and discrete outcomes focused on comparing proportions. All of the tests presented in the modules on hypothesis testing are called parametric tests and are based on certain assumptions. For example, when running tests of hypothesis for means of continuous outcomes, all parametric tests assume that the outcome is approximately normally distributed in the population. This does not mean that the data in the observed sample follows a normal distribution, but rather that the outcome follows a normal distribution in the full population which is not observed. For many outcomes, investigators are comfortable with the normality assumption i. It also turns out that many statistical tests are robust, which means that they maintain their statistical properties even when assumptions are not entirely met.
Rahimpoor, M. International Journal of Industrial Mathematics , 9 1 , Rahimpoor; A. Heshmati; A. International Journal of Industrial Mathematics , 9, 1, , International Journal of Industrial Mathematics , ; 9 1 :
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