The user specifies any two of these three quantities and the program derives the third. A description of each calculation, written in English, is generated and may be copied into the user's documents. Interactive help is available. The program provides methods that are appropriate for matched and independent t-tests,  survival analysis,  matched  and unmatched   studies of dichotomous events, the Mantel-Haenszel test,  and linear regression. It can plot graphs of any two of these variables while holding the third constant.
Power? What Power?
Linear or logarithmic axes may be used and multiple curves can be plotted on each graph. Graphs may be copied and pasted into other documents or programs for further editing. From Wikipedia, the free encyclopedia. Plummer Stable release 3. New York: Oxford U. Press; Statistical software. Next we will outline three approaches A, B and C. In this approach, we want to plan a fairly definitive study and have plenty of time and funding.
What power should we use? What size difference are we looking for? While we haven't yet studied people with hypertension, we know that other studies have found that the average number of receptors per platelet is about How large a difference would we care about? Let's say that our budget or patience only lets us do a study with 11 subjects in each group.
How much information can we obtain? Is such a study worth doing?
With a small study, we know we are going to have to make do with a moderate amount of power. In that case, what's the point of doing the experiment? We want more power than that, but know we can't have a huge amount of power without a large sample size. This sample size analysis has helped us figure out what we can hope to learn given the sample size we already chose.
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Now we can decide whether the experiment is even worth doing. Different people would decide this differently. How can all three approaches be correct? If you specify exactly what power you want, and how large an effect you want to detect, StatMate can tell you exactly how many subjects you need. But generally, you won't be sure about what power you want or are willing to accept or how large an effect you want to detect.
Therefore, you can justify almost any sample size. It depends on how large a effect you want to find, how sure you want to be to find it power , and how willing you are to mistakenly find a significant difference alpha. So there is no one right answer. It depends on why you are looking for a difference and on the cost, hassle and risk of doing the experiment. Graph the relationship between N and power StatMate does not create graphs itself. But if you own a copy of GraphPad Prism version 4. Each curve is for a different power, and shows the relationship between the sample size you could choose for each group X and the difference you would then detect as "significant" Y.
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As you go from left to right, the curves go down. This makes sense — if you use more subjects collect more data , then you'll be able to reliably detect smaller differences. Each curve is for a different power. If you choose a higher power, the curve shifts to the right. This also makes sense — if you want more power to have less chance of missing a real difference , then you'll need more subjects.
We'll choose the sample size chosen in approach B. In step 3 of StatMate, each value is a link. The screen shot below shows the first two of four sections of the report: a reiteration of your choices, and a detailed interpretation. The report then shows the entire table of tradeoffs which you have already seen and a discussion of when it makes sense to design studies with unequal sample sizes. You may view the entire report for this example as a pdf file.
You can send the entire report to Word with a click of a button Windows only , or via copy and paste. Using StatMate is entirely self-explanatory, and this example discusses the logic behind power analysis more than the mechanics of using StatMate. Learn the basic concepts of statistical power.
We will continue analyzing the experiment discussed in the sample size example Clinical Science , We determined the number of alpha2-adrenergic receptors on platelets of people with and without hypertension.
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Here are the results: Controls Hypertensives. The data was analyzed with an unpaired t test. Here are the results from Prism: Because the mean receptor number was almost the same in the two groups, the P value is very high. These data provide no evidence that the mean receptor number differs in the two groups. One approach is to interpret the confidence interval. Enter the results of the study.
Note that you do not need to enter the mean of the two groups. Mean values are not needed for power calculations. You need only enter the size and variability of the groups. StatMate shows us the power of the study given the sample sizes and standard deviations you entered to detect various hypothetical differences delta. The screen shot above shows the first two of three sections of the report: a reiteration of your choices, and a detailed interpretation. The report then shows the entire table of tradeoffs which you have already seen. You can export the report to Word by clicking one button Windows only or via copy and paste.
Calculate sample size — How many subjects data points do you need? Naturally, the answer is "it depends". It depends on how large a difference you are looking for, how much your data vary, and on how willing you are to risk mistakenly finding a difference by chance or mistakenly missing a real difference. StatMate helps you see the tradeoffs, so you can pick an appropriate sample size for your experiment. Calculate power — Just because a study reaches a conclusion that the results are "not statistically significant" doesn't mean that the treatment was ineffective.
StatMate calculates the power of a test to detect various hypothetical differences. Your sample size and power wizard. Why Choose StatMate? StatMate shows you the tradeoffs Some programs ask how much statistical power you desire and how large an effect you are looking for and then tell you what sample size you should use.
What about power? Introduction It is easiest to understand sample size calculations in the context of an example.
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For this example, we want to compute sample size for a new study. Later, we'll go through an example of determining the power of a completed experiment. What is your experimental design? In this example, we plan to compare the mean of two groups using an unpaired t test. Step 3: View tradeoff of sample size and power Some programs would ask you at this point how much statistical power you desire and how large an effect size you are looking for.
Approach A. That is a lot of subjects.
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Approach B shows an approach that justifies fewer subjects. Approach B. In this approach, we want a smaller sample size, and are willing to make compromises for it.