Now suppose the dietician can only collect data on 60 subjects with 30 in each Then our sample size will reduce by 4 for each group as shown below. Let’s say instead of using alpha level of. One way of reducing the sample size is to increase the Type I error Now the dietician may feel that a total sample size of 84 subjects is beyond herīudget. The results should be the same, and they are. Now, let’s useĪnother pair of means with the same difference. The calculation results indicate that we need 42 subjects for diet A andĪnother 42 subject for diet B in our sample in order the effect. 05.įor this example we will set the power to be at. The default significance level (alpha level) is. Pooled standard deviation, which is the square root of the average of the two Since what really matters is theĭifference, instead of means for each group, we can enter a mean of zero for Group 1Īnd 10 for the mean of Group 2, so that the difference in means will be 10. (diet A) and the mean for Group 2 (diet B). We first specify the two means, the mean for Group 1 For example, we can use the pwr package in R for ourĬalculation as shown below. In R, it is fairly straightforward to perform power analysis forĬomparing means. What the means are as long as the difference is the same. This isīecause that she is only interested in the difference, and it does not matter Group, instead she only specified the difference of the two means. Notice that in the first example, the dietician didn’t specify the mean for each Power this is the situation for Example 2. The pre-specified number of subjects for calculating the statistical.The pre-specified level of statistical power for calculating the sample.Probability of rejecting the null hypothesis when it is actually true.Ĭommon practice is to set it at the. The alpha level, or the Type I error rate, which is the.
Sample size for a specified power as in Example 1. There are two different aspects of power analysis. Subjects to detect the gender difference. Wants to know what the statistical power is based on his total of 40 The audiologist then measured the response time – the timeīetween the sound was emitted and the time the button was pressed. Each subject was be given a button to press He took a random sample of 20 male and 20 female subjectsįor this experiment.
He suspected that men were better atĭetecting this type of sound then were women. Response time to a certain sound frequency. An audiologist wanted to study the effect of gender on the The number of subjects needed in each group assuming equal sized groups.Įxample 2. She also assumes the standard deviation of blood glucose distribution for dietĪ to be 15 and the standard deviation for diet B to be 17. She also expects that the average difference inīlood glucose measure between the two group will be about 10 mg/dl. Of the experiment, which lasts 6 weeks, a fasting blood glucose test will beĬonducted on each patient. She plans to get a random sample ofĭiabetic patients and randomly assign them to one of the two diets. She hypothesizes that diet A (Group 1) will be better thanĭiet B (Group 2), in terms of lower blood glucose. A clinical dietician wants to compare two different diets, AĪnd B, for diabetic patients.
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