Suppose Dr. Cook is testing her expectation that her experimental group will have a higher mean than her control group. She gathers her data. Now that she has her data file, what are the steps she should take to test this question?
What will be an ideal response?
Ans: Varies.
Dr. Cook should begin with a visual inspection of her data. By creating frequency distributions, such as histograms, for her two groups she can visually evaluate their normality, looking for both skew, modality, and outliers, and she can get a sense of their central tendency and variability. Creating a frequency distribution of the key variables of interest is an important first step in data analysis. She should then compute descriptive statistics for both groups, including an index of central tendency and variability. In order to determine which measure of central tendency she should use, Dr. Cook will need to consider her dependent measure’s level of measurement. Assuming it is interval or ratio (and oftentimes ordinal), she will likely compute the mean and the standard deviation. Her next step is to test her hypothesis using inferential statistics. She will use a t test for two independent groups to test for statistical significance. She will use a null hypothesis of no difference between groups, compute the ratio of differences in means over total variance, and evaluate the likelihood of her result given the (counterintuitive) expectation that there is no difference between means. If her result is highly unlikely (less than 5%), then she can reject the null hypothesis.
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