7 common errors

Some common errors arise again and again in statistics.
Here are seven to watch out for:

1. unclear basepoint for graphs

A TV advert used to proudly proclaim: “X has 25% more active ingredient”
The screen however showed just the top of four test tubes. The words may have been true, but it looked a lot more than 25% – a truthful advert?

2. %increases and changes in %difference

If 300 is 50% bigger than 200, is 200 50% less than 300?
In 1990 product A had 10% of market share and now it has 15%. Is that a 50% increase or a 5% increase? Of course, the market may be only half as big now, so there may be less of product A sold.
Lesson – be very careful with your language.

3. beware extrapolation
Interpolation – estimating unknown values between known values – OK, but extrapolating – going outside the known limits is dangerous (albeit sometimes necessary!).

4. statistical significant ≠ important
Real, non-random effects may nevertheless be very small
Assuming that a non-significant result means no difference is like Kate Winslett assuming she weighs nothing because there was no detectable change in the waterline of the Titanic when she jumped off.

5. non-significant ≠ no effect
Big effects may not be significant if sample size is low or variability high.

6. relationship ≠ causality
There may be a common cause, or it may simply be a fluke!

7. don’t do too many tests!!
5% significant means will happen by chance 1 time in 20. If you do lots of tests, 1 in 20 will (on average) be 5% significant. So, if you need to do lots of tests look for better significance values.


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