What to trust in a "post-truth" world

There are many common criticisms of capitalism — CEO pay is not linked to performance, shareholder activism pressures companies to be short-termist, and share buybacks line investors' pockets at the expense of society. In contrast, it’s often claimed that ethical business always pays off. These claims are not based on evidence but have become widely accepted due to confirmation bias. This book sets the evidence straight but is far from a defense of capitalism. Instead, it argues that business needs to be urgently reformed to serve wider society — but a grounding in evidence is critical to inform us about the most effective solutions.

This book highlights the many ways in which we are frequently misled by data – falling for claims of causation when the data only supports correlation, not recognising that data may be a selected sample that only presents a small part of the picture, and ignoring researchers’ incentives to hand-pick the methodology that gives them the results they want. This rigorous but highly readable book explains how to spot the common ways in which people mislead us, either deliberately or unintentionally, and call it out in a constructive and professional way.

A simple cut-out-and-keep user’s guide to help discern whether an academic study is reliable.

In today's big-data world, it's very easy to find statistics that are correlated with each other. It's also easy to claim causation — particularly if the story is one that people want to believe in. This article explains how to tell if people are trying to pull the wool over your eyes — and how you can legitimately claim causation rather than just correlation.

This article summarizes the academic research on "confirmation bias" — the temptation to accept any evidence that supports your pre-existing viewpoint, and reject any evidence that contradicts it.

This insightful book highlights the value of using evidence to spur continuous improvement. In particular, it stresses the need to consider all evidence — including evidence that makes you feel uncomfortable — and the dangers of jumping to your preferred interpretation of a particular set of data.