When should I use the X2 Goodness-of-fit test (numerical data to compare with discrete probability distribution)? Remember that some tests, such as chi squared, can be used under various circumstances. The goal of the test changes based on the situation. Pay attention to the specific conditions noted in parenthesis to ensure you are picking the correct goal. A. Compares numerical data to a known mean. The null hypothesis is that the mean of the data equals the known mean. B. Test the fit of the normal distribution to the data set. C. Compare two treatment groups of independent samples where the data meet the assumption that the data fit the normal distribution. D. Test to compare frequency data to a specific population model E. Compare two treatment groups consisting of independent samples with a normal distribution AND unequal variance. F. Compare two treatments consisting of paired data where a normal distribution can be assumed. G. Test if the median of a data set equals a null hypothesized value when the distribution of the data does not meet the assumption of normalacy. H. More than two treatment groups where a normal distribution can be assumed. O 1. Compare two treatment groups when a normal distribution cannot be assumed. OJ. Test to see if the frequency data from a population fit a discrete probability distribution. O K. Compare categorical frequency data with an expected population proportion. No difference between observed and expected proportions is used as the null hypothesis. L. Compare more than two treatment groups when a normal distribution cannot be met. M. Compare two treatment groups consisting of paired data when the data do not fit the normal distribution.

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Types of Bias

Statistics is the study of data collection, organization, analysis, interpretation, and presentation. Statistical bias is a characteristic of a statistical technique or its findings in which the expected value deviates from the actual root quantitative parameter being estimated. According to the actual definition of bias, it refers to the tendency of a statistic to overestimate or underestimate a parameter.