An article written by Tim Harford describes the use of Bayes’ rule in order to make good and accurate guesses. Bayes’ rule describes the probability of an event, based on conditions that might be related to the event. There is an exact equation for Bayes’ rule and it is as follows:

P (A|B) = P (B|A) * P (A) / P (B)

Harford explains that when considering an event in which a guess or assumption can be drawn from it, you should take and combine any related information you have about the event. Considering all of this information, you will be more likely to make an accurate guess about the event that is occurring. He explains that psychologists have asserted that people do not make good guesses because they ignore some piece of obvious information altogether. Harford proposes the the information that gets ignored, usually a quantitative number, is the base rate and the effects of neglecting the base rate has been known since the 1950s.

He gives an example that asks him to guess what will happen the UK economy in 2016. Using his reasoning, one would suppose that there is a 10 percent change the UK economy will begin a recession this year. The logic behind this is in the fact that there have been seven recessions in the past 70 years. In this case, the base rate or ignored information is the 10 percent. Harford goes on to explain other ways this reasoning process can be applied. In particular, he talks about its usefulness in the field of screening programs such as DNA tests for potential criminals.

He concludes that it is extremely easy to jump to conclusions about probability. He emphasizes that it is important to take a step back and consider all of the information before you go and make a guess about something. He proposes that you should get in the habit of finding the base rate of any event and use the Bayesian way of thinking whenever you are trying to make a good guess.

ft.com/…/7d01cd92-da87-11e5-98fd-06d75973fe09

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