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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