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We performed simulations of Welch’s t-test and the classical 2-sample t-test on 10,000 pairs of independent samples generated from normal, skewed, and contaminated normal (equal and unequal variances) populations.
A healthcare consultant wants to compare the patient satisfaction ratings of two hospitals. The consultant collects ratings from 20 patients for each of the hospitals. The consultant performs a 2-sample t-test to determine whether there is a difference in the patient ratings between the hospitals.
2-Sample t. Learn more about Minitab. Use 2-Sample t to do the following: Determine whether the population means of two independent groups differ. Calculate a range of values that is likely to include the difference between the population means.
Complete the following steps to interpret a 2-sample t-test. Key output includes the estimate for difference, the confidence interval, the p-value, and several graphs.
Before collecting the data for a 2-sample t-test, the consultant uses a power and sample size calculation to determine the sample size required to detect a difference of 5 with a probability as high as 90% (power of 0.9).
Use Power and Sample Size for 2-Sample t to examine the relationship between power, sample size, and the difference when you want to compare the difference between two population means.
In Expression, enter SQRT ( (SUM ( (C1 - C3)**2)) / (total number of observations - number of groups)) . For the previous example, the Expression for the pooled standard deviation would be: SQRT ( (SUM ( ('Response' - 'Mean')**2)) / (6 - 2)) The value that Minitab stores is 3.75489.
Find definitions and interpretation guidance for every statistic and graph that is provided with Power and Sample Size for 2-Sample t.
Complete the following steps to interpret Power and Sample Size for 2-Sample t. Key output includes the difference, the sample size, the power, and the power curve.
If you provide values for power and difference, Minitab calculates the value of the sample size. For these two cases, Minitab uses an iterative algorithm with the power equation. At each iteration, Minitab evaluates the power for a trial sample size or trial difference value, and stops when it reaches the values you request.