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The Importance of the Normality Assumption in Large Public Health Data Sets Form

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data that has been sampled from a normal or Gaussian distribution is one of the main assumptions of parametric statistical tests such as t-tests and anova x' but how exactly can you decide whether data has been sampled from a normal distribution in this video I'll give you an overview of the main ways that are used to determine normality of data so there are two main ways that are commonly used to deduce whether data has been sampled from a normal distribution the first is the analysis of graphs such as QQ plots and frequency distributions the other is what are known as normality tests and these include the D'Agostino Pearson omnibus test the Shapiro Wilk test and the kolmogorov-smirnov test but before we go into the tests let's ask ourselves why we do normality tests in the first place let's say we have a population of a hundred people and we are interested in the height of these people so these 100 people make up our population we can measure t

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