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Data Analysis:Parametric inferential tests will be preferred over non-parametric tests, unlessdata deviate strongly from assumptions of parametric procedure. [Parametric tests are based onassumptions about the distribution of the underlying population from which the sample wastaken. The most common parametric assumption is that data are approximately normallydistributed.] Inferential analysis will be univariate (one predictor variable and one outcomevariable). Inferential tests designed to assess groups that are unmatched/independent will beused.NB. Data are to be reviewed to make sure they meet the parametric and individual assumptionsmade by each statistical test. If determined that assumptions of the parametric procedure arenot valid, use an analogous nonparametric procedure instead.[The reason we do statistics is to make inferences from samples to populations. To do this wehave to assume that the populations have certain properties so that the theoretical statisticalmodels we adopt are appropriate for making these kinds of inferences.]Assumptions of Confidence intervals:
Data Analysis:Parametric inferential tests will be preferred over non-parametric tests, unlessdata deviate strongly from assumptions of parametric procedure. [Parametric tests are based onassumptions about the distribution of the underlying population from which the sample wastaken. The most common parametric assumption is that data are approximately normallydistributed.] Inferential analysis will be univariate (one predictor variable and one outcomevariable). Inferential tests designed to assess groups that are unmatched/independent will beused.NB. Data are to be reviewed to make sure they meet the parametric and individual assumptionsmade by each statistical test. If determined that assumptions of the parametric procedure arenot valid, use an analogous nonparametric procedure instead.[The reason we do statistics is to make inferences from samples to populations. To do this wehave to assume that the populations have certain properties so that the theoretical statisticalmodels we adopt are appropriate for making these kinds of inferences.]Assumptions of Confidence intervals:
Test Assumptions
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Parametric Assumptions. Listed below are the most frequently encounteredassumptions for parametric tests. Statistical procedures are available for testingthese assumptions. The Kolmogorov-Smirnov Test is used to determine how likely it is that a sample came from a population that is normally distributed.
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Choosing Between a Nonparametric Test and a Parametric ...
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Feb 19, 2015 - Nonparametric tests are also called distribution-free tests because they don't assume that your data follow a specific distribution. You may have heard that you should use nonparametric tests when your data don't meet the assumptions of the parametric test, especially the assumption about normally distributed data.
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[PDF]Parametric and Nonparametric: Demystifying the Terms
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assumptions that are at least of a moderately general nature hold.” That definition ...Parametric tests and analogous nonparametric procedures. As I mentioned ...[PDF]Error Type, Power, Assumptions Parametric Tests ...
https://www.ohio.edu/plantbio/staff/.../Error&Power.pdf
1. Error Type, Power, Assumptions. Parametric vs. Nonparametric tests. Type-I & -
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lecture 2: the assumptions of parametric statistical analysis
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ANOVA tests to see if the central tendency (mean) difference between samples ...assumptions that are made when using parametric statistical techniques:.Assumptions For Statistical Tests | Real Statistics Using Excel
www.real-statistics.com/descriptive-statistics/assumptions-statistical-test/
When these are not met use non-parametric tests. ... Explain the reason for using ANOVA, do you the data collected meet parametric statistical assumptions?Parametric tests - Website of Dr. Chong Ho (Alex) Yu
www.creative-wisdom.com/teaching/WBI/parametric_test.shtml
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In a parametric test a sample statistic is obtained to estimate the population ... that many parametric tests are not seriously affected by violation of assumptions.Nonparametric: Distribution-Free, Not Assumption-Free
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Table 1 contains the most commonly used parametric tests, their nonparametric equivalents and the assumptions that must be met before the nonparametric testParametric and Non-Parametric - VassarStats
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In the literal meaning of the terms, a parametric statistical test is one that makesassumptions about the parameters (defining properties) of the population ...Parametric statistics - Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Parametric_statistics
When the assumptions are correct, parametric methods will produce more accurate and ... If we assume all 99 test scores are random samples from a normal .
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Data Analysis:Parametric inferential tests will be preferred over non-parametric tests, unlessdata deviate strongly from assumptions of parametric procedure. [Parametric tests are based onassumptions about the distribution of the underlying population from which the sample wastaken. The most common parametric assumption is that data are approximately normallydistributed.] Inferential analysis will be univariate (one predictor variable and one outcomevariable). Inferential tests designed to assess groups that are unmatched/independent will beused.NB. Data are to be reviewed to make sure they meet the parametric and individual assumptionsmade by each statistical test. If determined that assumptions of the parametric procedure arenot valid, use an analogous nonparametric procedure instead.[The reason we do statistics is to make inferences from samples to populations. To do this wehave to assume that the populations have certain properties so that the theoretical statisticalmodels we adopt are appropriate for making these kinds of inferences.]Assumptions of Confidence intervals:
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