Size Matters

"Over 80% of women say that shampoo leaves their hair healthier and shinier".
Such claims are common in advertising for all manner of consumer products. What they might not tell you is that only five women tested the shampoo. And of the four who certified its miraculous effect, one or two probably ended up with nicer hair purely by chance, or simply imagined the results.

When I buy a new shampoo that promises to deliver bouncy, shiny and luscious hair, I usually fall under the latter of the group and simply will myself to believe that my hair is indeed much bouncier, shinier and luscious. Of course, I’m sure it’s just all in my head. I don’t think it makes any particular difference.

Similar caveats can apply to the effectiveness of all other things we use in our daily lives. In medicine, curing 6 out of 10 patients is promising. Curing 300 out of 500 is the same success, but far more convincing.

Sample size in a test or experiment is absolutely crucial in deciding whether any apparent improvement may have taken place or if it all by just chance alone. The other thing to look out for is randomized controlled trials (RCT) where volunteers are randomly assigned to the experimental group that receives treatment and a control group that receives a placebo. This just evens it all out a little better. The last thing to keep your eye on is making sure the test is repeated over several trials at different times with different participants. If the statistical significance of the test is higher, the more successful it is.

So next time you hear of public accliam for a miracle cure or a wonder shampoo, ask three questions: How many people was it tested on? Was it an RCT? And was the result confirmed by a second, independent test?

Be a skeptic! Don’t take things at face-value.

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