The workbook is a recipe for calculating a required Sample Size
for two independent Proportions given Alpha, Power (1-Beta) and Effect Size.
It is applied to a binary (categorical) outcome measure,
such as web-ad click-through rate (CTR), or child mortality rate.
A common case is a split-test (A/B test).
When designing such tests (experiments)
one should define a-priori minimal sample size(s)
for a test group (A) and a control group (B) to get a result
which is both statistically significant (defined by Alpha parameter)
and has enough statistical power (defined by Beta parameter).
But this calculation could also be applied Post-hoc --
to estimate which groups (test cells) have been designed
to have sample sizes well enough to validate differences
in measured binary outcome(s) between them.
As an example of a latter approach,
data from The Global Slavery Index 2014
http://www.globalslaveryindex.org/
were used in this workbook.
As found in their methodology paper
http://d1p5uxokz2c0lz.cloudfront.net/wp-content/uploads/2015/01/GSI2014_full_methodology_new-op.pdf ,
data for the seven countries (including Russia) had been obtained from the Gallup World Poll 2014 survey.
And the poll sample sizes are seen in Table 2 on page 5 of this document.
So I combined these data together with the main GSI-2014 results
in an Excel spreadsheet and use it as a Tableau datasource (in the attached).
Link to a workbook on Tableau Public:
https://public.tableau.com/views/Z-TestProportionsSampleSize/Dashboard7
Hope it could help someone.
Yours,
Yuri
PS The logic behind the calculations could be found in this article:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3672437
General definitions are in Box 1, and the exact calculations and definitions
for a categorical outcome measure are shown in Box 3 of the article.
One could download a PDF version of the article here:
http://canjsurg.ca/wp-content/uploads/56-3-207.pdf
More subjects on a topic:
1. G*Power : statistical power analysis tool
Universität Düsseldorf: G*Power
2. G*Power Data Analysis Examples: Two independent proportions power analysis
3. How large should your A/B test sample size be? - VWO Blog
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