Section VI

Section VI - Section VI Your Results How To Analyze...

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Section VI. Your Results: How To Analyze, Interpret, and Report Your Observations Results Provide thorough description of data (in terms of level of measurement, quality, etc.) The application of statistical procedures to answer research questions and/or test hypotheses Statistics Basic uses of statistics: To Describe - Statistics can be used to describe the results of tests on a sample in terms of central tendency (mean, median, mode), dispersion (standard deviation, variance), relationships among or between variables (correlation coefficients), predictions (regression, LISREL analysis), or differences between treatment groups (chi-square, t- tests, anovas, etc. . To Infer - These same statistics can be used to make inferences from the sample to a larger population, the difference being that inference introduces a random sampling error band around all estimates (means, proportions, standard deviations, correlations, beta weights, etc.). Which statistical test should be used depends on the purpose (to describe or infer) and the assumptions that can be met (the level of data and normalcy of distribution). Application of the wrong test will produce uninterpretable and incorrect results. There are also ethical considerations. Parametric statistics: Have a greater statistical power than non-parametric at a fixed sample size, i.e., they are more sensitive to differences than non-parametric Three assumptions must be met before applying parametric statistics to data: the data must be interval or ratio level, population distributions must be normal and variances in populations must be homogeneous. In other words, assumptions are made about population parameters; hence the term parametric statistics Non-parametric statistics: Non-parametric statistics do not make assumptions about population parameters. They are also called "distribution-free" statistics since no assumption of normality is made.
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Should only be used with nominal or ordinal data and for interval data with abnormal distributions and/or large differences in variances (there are tests to determine normality (chi-square Goodness of Fit test) and homogeneity (F max test) Statistical Testing: Deciding between a null and alternative hypothesis (also refer to other notes in reader on statistics) p value: The p value is the probability that the observed difference between treatment groups in an experiment or an observed correlation is due to chance. It is the result of the statistical analysis of your data. power analysis:
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Section VI - Section VI Your Results How To Analyze...

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