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 those responsible for deciding whether contaminated land needs to be made safe for human use on how to use statistics to make their decisions. 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 two statements published by the ASA (American Statistical Association) on the use & misuse of P-Values in 2016 & 2019 and in writing this guidance I felt was I an ambassador for turning those statements into something that could used by non-statisticians to make real life decisions that have an impact on us all.
Updated on 14th May 2020. New and modified links are italicised.
The Coronavirus Pandemic is a worldwide challenge many of us will have not experienced before. It is natural to want to seek information on the risks and in our world today, it has never been easier to find data, analyses and opinions. Unfortunately, a lot of what you will read out there is either unhelpful or actively misleading. As an independent statistician with 30 years experience of explaining statistics to non-statisticians, my contribution to this crisis will be to try and sort the good from the bad hence this post. [Read more…] about Coronavirus #1 – Useful Data and Links
The city of Bath is among a number of cities in the UK tasked with reducing Nitrogen Oxide (NOx) emissions. NOx pollution is thought to contribute to poor health and the government has required clean air plans from the relevant local authorities to be in place before 2021. I had no idea that this would result in my statistical expertise being needed to answer a political row over the BathBreathes2021 plans to charge cars driving into Bath and you can read my report to see what my answer was!
On June 5th 2019, I had the privilege of being able to talk to the Treasury Select Committee about the “Effectiveness of Gender Pay Gap Reporting“. My name was put forward by the Royal Statistical Society and we spent an hour discussing a number of issues with a particular focus on the Finance sector.
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.
April 2018 was the deadline for submitting gender pay gap results and we now have the first detailed picture of how pay differs between men and women in the UK. A nifty government website can be used to look up pay gap details for any company employing more than 250 employees and you can also download the results for further analysis. So what will happen next? Will the data be used properly to inform debate about how men and women are paid or will it be misused for personal and political gain?
I believe this data can be of benefit to the debate around gender equality but my fear is that to begin with, it will be misused, misinterpreted and reinforce the saying “lies, damned lies and statistics”. So if you want to misuse gender pay gap data, who better to ask that a professional statistician like me who will show you how you can do this by commenting on 7 plausible statements.
Fake news has entered the political dictionary over the last year. Suddenly, politicians and commentators are worried that elections are being influenced by false stories being circulated that appear to be genuine. Social media platforms are under pressure to filter out such stories raising the old questions of censorship and “who guards the guards?” However, evidence on the extent and influence of fake news is thin on the ground.
Welcome to my first post where I put my Evidence Hierarchy or Circle into practice and show you what is behind the headline.
Today I am concentrating on science and technology related articles from the BBC website since that is accessible to nearly everyone. As always, I am critiquing the article more than the research since I have not read the research papers that motivated the article. The 3 articles are:
- “Fruit shaped sensor can improve freshness“.
- “Robots to affect up to 30% of jobs“
- “Dinosaurs may have UK origin“
“Graduates aren’t skilled enough!” says a BBC headline. What is your immediate reaction? If you decide to find out more and read the article, you will see the following.
- A brief reference to a survey of a 174 organisations, half of whom are apparently moaning graduate skills.
- 3 brief interviews with recent graduates asking what they wish they had learned before starting their job.
After reading this, do you feel that a case has been made that universities are slipping up? How much weight should you place on this article and the information it contains? One of the major problems with news these days is that we are bombarded with articles about so many things that it can difficult to sort the good from the bad, especially when articles are referring to data in one way or another. My Evidence Hierarchy provides a short cut to assess the usefulness of news articles and with a bit of practice, I hope the result will be less stress for you about what is going on in the world.