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Comparing the Means of Two or More Groups

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Comparing the Means of Two or More Groups
When it comes to comparing the means of two or more groups, we must state that a null hypothesis. Random samples of each population must be chosen to be able to show the formula in a working status. To get a more accurate sample, a higher number of the populations should be used. There are two tees that can be used for testing depending on the sample size and if the standard deviation is known. The t-test, which test of whether a sample observation comes from a larger sample with standard distribution of statistical properties. The z-test, “a statistical test used to determine whether two population means are different when the variances are known and the sample size is large.” (Investopedia, 2015) If the standard deviation is known then the z-test would be used, and when it is not known we would use the t-test. (Lind, Marchal, & Wathen, 2011) To determine whether a sample is large the sample amount needs to be more than 30.
When there are more than two means to compare, the more efficient method is an analysis of variance, or ANOVA. An ANOVA is especially valuable for randomized sampling designs (McClave, Benson & Sincich, 2011). Pooled variance can be used if the variances of the two populations being tested are constant.
Reference
Investopedia. (2015). Retrieved from Investopedia: www.investopedia.com/terms/z/z-test
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic Statistics for Business & Economics, Seventh Edition. McGraw-Hill Company.
McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11thed.). Boston, MA: Pearson-Prentice

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