![\"\"](\"http:\/\/www.economics.soton.ac.uk\/staff\/aldrich\/fisherguide\/Doc1_files\/image001.gif\" "\\"Fisher")
It’s worth taking some time to return to basics every now and again, and one thing that continues to befuddle medics the world over is the issue of ‘statistical significance’. <\/p>\nIt’s worth taking some time to return to basics every now and again, and one thing that continues to befuddle medics the world over is the issue of ‘statistical significance’. <\/p>\n
Take your average trial, say 3% saline vs. placebo for bronchiolitis admissions. You can take the proportion of kids who end up in hospital in each group, and then ask the questions “is this difference just a chance finding, or not?” You – or someone else – then does a statistical test. The test then gives you an answer, which tells you is how likely the result you found is to be due to chance: the p-value.<\/p>\n
So what? If p = 0.05, does this mean that 3% saline works? Well, it means that the results, if coming from unbiased samples, are likely to be this or even more different in only 1 in 20 cases. (If the p=0.01, then this would be by fluke in only 1 in 100 cases.) You’ll note the p-value never tells you something works, or not, it just tells you about chance. By convention, we have decided that if it’s “true” if p<0.05. But this is a convenience and a fascinating set of debates surround this<\/a> value.<\/p>\n If you think that’s good enough, then take the leap of faith, and prescribe.\u00a0 If you’re looking for certainty, then get a different job. If instead of just saying ‘Does it work?’ you want to know ‘How well does it work?’ then you don’t want statistical significance – you want an estimate of effect: more about that in another blog.<\/p>\n Acknowledgment: Fisher photo from Zephyrus<\/a><\/p>\n ps – Monthly emails explaining statistics from here<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" It’s worth taking some time to return to basics every now and again, and one thing that continues to befuddle medics the world over is the issue of ‘statistical significance’. […]<\/p>\n