The Importance of Testing Assumptions Before Running Statistical Analyses

Thursday June 14, 2012

Elite Research explores the importance of testing statistical assumptions. Elite Research is a global provider of research design and statistical consulting. They support academic, corporate, medical/health, and non-profit researchers in designing, collecting, analysing, and reporting efficient and accurate results.

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; rather, they assume that these have been met as they are conditions in which the logarithmic functioning of the program underlie. Therefore, researchers must thoroughly explore the data and run appropriate preliminary analysis to ensure that the data does not violate the assumptions of the statistics planned to be used.

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.

Contact Elite Research today to get reliable help with all of your statistical or editorial needs!
www.eliteresearch.com or (800) 806 – 5661.

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