Sample Size Calculator

using statistical power

OBJECTIVES

 

  1. Understand power and sample size estimation.

  2. Understand why power is an important part of both study design and analysis.

  3. Understand the differences between sample size calculations in comparative and diagnostic studies.

  4. Learn how to perform a sample size calculation.

     

    • – (a) For continuous data

    • – (b) For non-continuous data

    • – (c) For diagnostic tests

     

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simple random samples only

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When you survey a large population of respondents, you’re interested in the entire group, but it’s not realistically possible to get answers or results from absolutely everyone. So you take a random sample of individuals which represents the population as a whole.

The size of the sample is very important for getting accurate, statistically significant results and running your study successfully.

  • If your sample is too small, you may include a disproportionate number of individuals which are outliers and anomalies. These skew the results and you don’t get a fair picture of the whole population.

  • If the sample is too big, the whole study becomes complex, expensive and time-consuming to run, and although the results are more accurate, the benefits don’t outweigh the costs.

 

Steps:

  • Select the Confidence Level you want to work at.

  • If you are sampling from a finite population (one that isn't very large), enter the Population Size.

  • If you already know the estimate Proportion, or want to check the Relative Standard Error of an existing estimate, fill in the Proportion. If left blank it will be assumed to be 0.5.

  • You must fill in one of the Confidence Interval, Standard Error, Relative Standard Error or Sample Size. Make sure the bullet point corresponding to the one you wish to specify is selected.

  • Press Calculate to perform the calculation, or Clear to start again.

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