Parametric statistical tests

Log normal distribution, the distribution of a sample that is normal only when plotted on a logarithmic scale. The most prevalent cases in pharmacology refer to drug potencies (agonist and/or antagonist) that are estimated from semilogarithmic dose-response curves. All parametric statistical tests on these must be performed on their logarithmic counterparts, specifically their expression as a value on the p scale (-log values) see Chapter 1.11.2. 

This procedure possesses the advantage that little or no prior habituation is required and that all latency measures can be employed for measuring drug effects, in principle allowing the use of more powerful parametric statistical tests. On the other hand, it is not clear whether the data obtained using this version of the procedure yields markedly different results from those obtained with the more simple version described above. 

The obtained results are reported in the following tables medians and non-parametric statistical tests were used because data did not approach a Gaussian distribution. 

When using transepidermal water loss or corneometer instrumentation, standard parametric statistics—/-tests or ANOVA—can be applied to the data. However, nonparametric statistical models are more appropriate than parametric ones for analyzing data from visual grading, a subjective rating system [17]. Nonparametric statistics apply rank/order processes that do not utilize parameters (mean, standard deviation, and variance) in evaluating data and also have the advantage that data need not be normally distributed, as is required for parametric statistics [18]. Thus, when using small sample sizes such as may be encountered in pilot studies where the data distribution cannot be assured to be normal, nonparametric statistics are preferred [3]. 

The two-sample t-test (or Student s t-tesi) is the most widely used parametric statistical test. This test compares the means of two populations that should be normally distributed when a sample size is small. The test statistic is formed as the mean difference divided by its standard error, that is, the difference of measured expressions normalized by the magnitude of noises. If the difference of the measured expressions is very large relative to its noise, it is claimed as being significant. Formally, suppose we want to test null hypotheses, H/. pji = pj2, against alternative hypotheses, Hj pji pp, foij= 1, 2,..., m. The test statistic for each j is... 

Danish mathematician George Rasch developed the Rasch model (5). Researchers can use the Rasch model to develop tests and surveys, monitor the quality of survey or test data, improve test or survey items, and calculate an equal interval total score for both test takers and survey respondents. When researchers evaluate data using parametric statistical tests (e.g., t-test, ANOVA), they assume that score data is " equal interval. We can use the Rasch analysis software to convert non-equal interval data into equal interval data. In recent years, evaluators have used the Rasch model for large-scale, assessment projects such as the evaluation of reform in the Chicago Public Schools (6)... 

Parametric A parametric statistical test specifies certain conditions about the distribution of the population from which it is drawn, eg, normality. The relevance of results depends on these assumptions being met. Measurements must be at least in an interval scale... 

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