3 ms·
The real problem is that effect size is rarely discussed. P-values only relate to variability, sometimes high variability is acceptable, sometimes it's not. T
by RA_Fisher 13y ago
The real problem is that effect size is rarely discussed. P-values only relate to variability, sometimes high variability is acceptable, sometimes it's not. Taken by itself, a p-value is worthless, no matter how small it is. For example, suppose you develop a fertilizer that you're 99.99999% certain will produce one additional ear of corn in 10,000 bushels. Who cares!? You see a lot of that in published papers. A lot of researchers stop once they get a p-value below 0.05 and neglecting effect size is the norm.
- ProblemFactory 13y agoEffect size is important, because all events in the real world except for true random numbers have some correlation. Eating an apple might help a broken leg heal faster or slower. There is certainly an extremely small correlation, and with a sufficiently large number of controlled experiments, a result rejecting the null hypothesis with p < 0.05 will be found. The required number of experiments might be astronomically large, but if any correlation exists, it can be found with enough samples. But without looking at the effect size, the result is useless. Even if eating an apple helps your broken leg heal 2 seconds faster on average, it is pointless to suggest this as medical advice.