In this episode, we discuss how dangerous statistics can be.  People often confuse statistics with facts, but anytime someone quotes a statistic, there are a few things you need to keep in mind.

This topic has bonus content

Thanks for joining me for another podcast.  I really appreciate all my new subscribers

and those of you that have left reviews on iTunes and other places.  As always, I appreciate both positive and constructive

suggestions. 

Today’s episode is:

Statistics: 9 common ways the numbers are lying to you

I’m not going to lie, this has been the most difficult

podcast to produce so far.  I’m not a

statistician, and trying to simplify complex statistical concepts is definitely

tricky.  I have at least 15 hours into

the production of this episode.  I hope

you enjoy it.

The content in this podcast is adapted from How to lie

with statistics by Darrell Huff.

As I mentioned in my intro podcast, statistics can actually

be quite dangerous

As a young grad student, someone much older and wiser than

me recommended I read How to lie with statistics.  It was spot on when it was first printed in

1954 and is still very relevant today

Today I am going to discuss some common pitfalls we can run

into when interpreting statistics

Statistics pitfall #1: Sampling bias

For a study to be accurate, it must faithfully represent the

population of interest.  For example, a

political telephone survey that uses only landline numbers misses a large

portion of the US population who only have cell phones.  No matter how much data a study collects, if

it doesn’t represent the group in question, it is pretty worthless.

The problem with studying humans, is that we can’t just

randomly assign people to different groups for the sake of science and so

almost all study participants self-select to some extent

This sampling bias can be conscious, or unconscious.  We can correct to some extent for conscious

bias, but unconscious bias is particularly dangerous because we often don’t

realize it is happening.

For example, when YouTube created a new video loading

feature, about 10% of videos were loaded upside down.  When they began trouble shooting why so many

users loaded them incorrectly, they discovered that most of the upside-down

videos belonged to left-handed people. 

Because of an unconscious bias towards right-handed people, the app left

out about 10% of the population. https://www.eliinc.com/five-real-world-examples-of-unconscious-bias/

Another example of self-selection bias occurred in Boston

University’s study of brain trauma in American football players.  The results of the study were widely reported

as “99% of football players had CTE” even though the researchers admitted that

the study population was biased.  How was

it biased? All of the 202 brains examined in the study were from players who

exhibited neurological symptoms while living. 

For the results of the study to be accurate to the population, brains

would have to be taken across a wide range of people who had at one point in

their life played football, not just those with symptoms.  Additionally, nearly half of the brains