Statistics are boring. They’re hard. They’re useless. You’ll never use them in real life.
Oh, how wrong that is. I’ll agree that if you aren’t blessed with the genes that make math and statistics a piece of pie (mmmm, pie), then yes statistics are hard. But there are innumerable real-life examples to show just how important it is to be comfortable with statistics.
Sports: If you’re a fan of sports, you no doubt are bombarded with statistics throughout the season just like these interpretations of statistics shared by James Conley on the Pensburgh. The headlines are exciting but the reality of each headline is simple – they mislead and even outright lie. If you understood statistics, you’d immediately see for yourself what the numbers really said.
HEADLINE! Pittsburgh’s penalty kill is going to be an Achilles’ Heel this season!
(The team has killed 18 straight chances over its last five games.)
BREAKING! The team is going to walk away with the Metropolitan Division again!<
(They’re in second place, and half the division is within a point of catching them.)
THIS JUST IN! The Penguins just can’t put away teams late in the game!
(They’ve outscored their opponents 5-0 in the third period of two straight games, both wins.)
Medicine: How many commercials on TV and ads in magazines extol the virtues of amazing new drugs, perhaps even drugs that you are desperate to try to alleviate your own health issues? If you understood statistics, you would know right away when the ads were misleading. You’d spot when the sample sizes were too small to be reliable, when the effect size was too small to be meaningful, or when the lack of a test-retest design suggested insufficient testing.
Sometimes companies egregiously exaggerate how well their drugs work. In a brochure given to doctors and nurses last year, the Japanese drug company Eisai claimed that its Dacogen drug helped 38% of patients with a rare blood cell disorder in a clinical study. This figure was false, the FDA said in a November 2009 warning letter. In fact, the figure was taken from a tiny subgroup of patients who responded well to the drug. When all patients in the study were included, the real response rate was a much less impressive 20%, the FDA noted.
Read more on this and other misleading advertisements here.
Politics: Political polling is becoming more and more prominent in the news. If you had a better understanding of statistics, you would know when to trust the polls. You would know why percentages don’t always add to 100, why polls ‘weight’ data, or why the margin of error is ridiculously important (even if you don’t have a random sample).
Seven hundred randomly selected New York likely voters were interviewed by landline and cell telephone between October 1 and November 1, 2014. The margin of sampling error is +/- 3.6 percent. The data have been weighted to adjust for numbers of adults and telephone lines within households, sex, age, and region. Due to rounding, percentages may not sum to 100%. Responder numbers in each demographic may not equal the total respondent number due to respondents choosing not to answer some questions.
No matter how you look at it, statistics are among the most important classes you can take. It’s in your best interest to sign up for a class now.
- Data Tables: The scourge of falsely significant results #MRX (lovestats.wordpress.com)
- Proud to be a member of survey research #MRX (lovestats.wordpress.com)
Who doesn’t have fond thoughts of 300 page data tabulation reports! Page after page of crosstab after crosstab, significance test after significance test. Oh, it’s a wonderful thing and it’s easy as pie (mmmm…. pie) to run your fingers down the rows and columns to identify all of the differences that are significant. This one is different from B, D, F, and G. That one is different from D, E, and H. Oh, the abundance of surprise findings! Continue reading →
Wouldn’t it be great it you could just read and interpret a number, and then be confident about your interpretation? If that was the case, you wouldn’t be able to buy 23 different books called “How to lie with statistics.”
Here are a few common problems I see when people try to interpret numbers.
Dislike matters just as much as like. Don’t get so focused on top box scores that you forget about bottom box scores. Brands can easily have identical top box scores and ridiculously different bottom scores.
How many times have you seen huge inexplicable spikes in your charts? Spikes are a key indicator that your sample size is too small. Be extremely nervous about numbers based on only 30 people. Be cautious of numbers based on fewer than 100 people. Check first and avoid embarrassing conclusions.
Everything on the planet is governed by rules. And one of those rules is randomness. When you’ve determined that a small sample size is not the cause of the spike, and there is no discernible explanation for the spike, consider that it may in fact be a random number. Random happens. Deal with it.
Just because a test came out significant today doesn’t mean it will with new data next week. See previous point. You will know you’ve really got something when its significant when it occurs on several unique occasions.
Have a look here too