Confidence Intervals

Stats Training Materials – Statistical Inference & Hypothesis Testing

March 8, 2025 By Nigel Marriott

Mention P-values and most people will probably shudder at some memory of an incomprehensible lecture or lesson on statistical tests. Words like null hypotheses, t-tests, statistical significance might pop into your mind with little understanding of what they are about. What you may know is that scientists have to report a p-value for any experiment they do or do they?

The area of Statistical Inference is a core area of study for any statistician. Put simply, Inference means to infer from the observations you’ve made about your data and to draw conclusions about what might be happening in real life. There are two parts to Inference.

Pay Gap Trends #7 – UK Ethnicity Pay Gap 2012 to 2022

January 3, 2024 By Nigel Marriott

In November 2023, the Office of National Statistics (ONS) resumed publishing estimates of the UK Ethnicity Pay Gap after a 3 year hiatus for the Covid19 pandemic. I have organised this data into a user-friendly (hopefully!) Microsoft Excel spreadsheet which allows you to see how the median hourly pay has changed between 2012 & 2022 for a variety of ethnicities & other categories. This article explains how you can use this spreadsheet and why you must look at ethnicity pay gap data in a different way than you would for gender pay gap data.

Data Driven Decision Making #1 – New Statistical Guidance for Land Contamination Surveys

October 11, 2020 By Nigel Marriott

On 30th September 2020, CL:AIRE (the industry body for the land contamination & remediation sector) published new professional guidance for “Comparing Soil Contamination Data with a Critical Concentration“. The 46-page document advises how to use statistics when assessing land contamination and deciding whether it is safe for development. I was the lead author of the guidance and I spent 4 years working with CL:AIRE’s steering committee on what the guidance should cover. The 4 years were bookended by statement & editorial published by the ASA (American Statistical Association) on the use & misuse of P-Values in 2016 & 2019 respectively and in writing this guidance I felt was I an ambassador for turning those into something that could used by non-statisticians to make real life decisions that have an impact on us all.