Many statistical tests have assumptions that must be met in order to insure that the data collected is appropriate for the types of analyses you want to conduct. Common assumptions that must be met for parametric statistics include normality, independence, linearity, and homoscedasticity. Failure to meet these assumptions, among others, can result in inaccurate results, which is problematic for many reasons. When testing hypotheses, running analyses on data that has violated the assumptions of the statistical test can result in both false negatives and false positives, depending on the particular assumption violated.
Most, if not all, statistical software packages, such as SPSS and SAS, do not automatically check these assumptions;
Elite Research’s team of qualified consultants can be a vital aide in all of your research needs, from data collection, data prep, testing assumptions, and primary analysis.
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