# USING G*POWER SOFTWARE (Calculating Power) BIOSTATSAnswers 2Bids 29Other questions 10,USING G*POWER SOFTWARE (Calculating Power) BIOSTATSAnswers 2Bids 29Other questions 10

USING G*POWER SOFTWARE (Calculating Power) BIOSTATSDUE 3/10/19  10 P.M ESTBE ON TIME AND ORIGINAL WORKHave SPSS SOFTWARE AND G POWERDONT ASK IF YOU DONT HAVE ITREAD INSTRUCTIONS CAREFULLYDATA ATTACHED AND HELPFUL STEP BY STEP GUIDE!!G POWER ATTACHED AS WELLIn research, calculating power is essential. The larger a sample size, the more powerful a study may be. It can be helpful to calculate the minimum sample size needed for basic statisticssuch as the t-testwithout the aid of software. Sample size calculation is based on the statistical testing method used. Remember, sample size denotes the number of variables examined in data collection.You use the software G*Power located to do your calculations. Calculating power based on sample size, rather than calculating sample size based on power, is useful when using secondary data where the sample size is already fixed.For this Assignment, you practice manually calculating basic sample size. You also practice calculating power with the assistance of G*Power software located in the Learning Resources.The AssignmentUsing the  Dataset (SPSS document) ATTACHED1. Calculate the minimum sample size you will need (using manual calculations) to conduct a t test to determine if the difference in age in people with and without hypertension is significant. Use the standard deviation for age calculated in week 1. Assume a 2-tailed test with alpha = .05 and Power = 80% and that the difference you expect to observe between the 2 groups is 5 years. 2. Using two different effect sizes in addition to the one used in part 1, perform three power analyses of the sample size computed in step 1 using G*Power. (Assume a 2-tailed independent sample t test with alpha set at .05) 3. Perform a power analysis using G*Power using the actual sample size presented in the dataset for week 1 (180) and an effect size of .30. What does this mean in terms of the study and the probability of experiencing a type 2 error?

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