Made Up Statistics in the Wild
Here is an interesting article from the UK:
“Numbers up: The truth about statistics”
And in the spirit of correlation of the week, here is a good excerpt from that article:
“Toothless post-menopausal women are three times as prone to hypertension as those with teeth”
This news, reported in the respected journal Hypertension, might have lead to queues of denture-wearing women of a certain age at GPs’ surgeries. A study by Japanese researchers from Hiroshima University, published in 2004, suggested that tooth loss in post-menopausal women was directly linked to high blood pressure, which can increase the risk of heart disease or strokes.
But a look past the headlines revealed a problem: the scientists based the conclusion on a study of just 98 post-menopausal women – 67 with missing teeth, and 31 with their gnashers intact. In statistical terms, that is an almost insignificant sample size.
The problem is that the apparent cause of a link can sometimes be pure chance. The smaller the sample, the more likely this becomes. One statistician famously managed to find a statistically significant correlation (in a small enough sample) between birth rates in various European countries and the stork population, suggesting the birds therefore really do deliver babies.
McConway’s verdict: “There’s no standard minimum group size for statistical studies – it depends what you’re measuring. If it’s something that doesn’t vary much – say, blood pressure in elite athletes – you could get away with a smaller group. But for something like this, you need a much larger sample.”