Nigel Marriott's Blog

An independent statistician using data to understand our world and to predict the future

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Stats Training Materials – Pay Gap Analytics

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*** Updated with links B16 and P9, P10 and P11 for 3 presentations I have given recently on gender & ethnicity pay gap reporting.  I have also added new sections I & J**

The 5 steps to closing a pay and/or representation gap are –

  1. Define what success looks like i.e. where you want to be in the future
  2. Measure where you are today
  3. Analyse your data to understand why you are where you are today & what the key drivers are of your gaps
  4. Implement actions which move you from where you are today to put you on the path to where you want to be in the future
  5. Control the key drivers to ensure you are on the right track

All steps require the use of statistical thinking and statistical methods.  Other skills and processes are also needed but they cannot succeed on their own without the help of statistical thinking.

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Pay Gaps #17 – How to Spot an Incorrect Median Gender Pay Gap

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I have a spotted an incorrect median gender pay gap published by a well known name in a certain industry.  They shall remain nameless for now since I am trying to get them to accept their error and publish a new gender pay gap report on their website.  I know they have made an error because their published data violates the laws of mathematics as I will explain in this blog.  All it takes to spot such an error is a simple calculation you can do in your head and an understanding what the median measures.

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Stats Training Materials – Basic Statistical Concepts

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A sound grasp of basic statistical concepts is essential to have any hope of acquiring the mindset of a statistical thinker and to be able to use statistical methods.  My introductory course “The 6 Concepts of Statistical Thinking” lays the foundations in the following.

  1. Probability – the difference between conditional & absolute probability.
  2. Risk – why it is an extension of probability and the importance of alpha (false positive) and beta (false negative) risk.
  3. Expectation – how to summarise a dataset into one number which measures its location.
  4. Variance – how to measure the spread of a dataset.
  5. Distribution – how to describe the shape of a dataset.
  6. Correlation – how to measure the relationship between two variables and understand the two golden rules of correlation.

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