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
were used in this workbook.
As found in their methodology paper
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:
Hope it could help someone.
PS The logic behind the calculations could be found in this article:
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:
More subjects on a topic:
1. G*Power : statistical power analysis tool