What is the p-value fallacy?
This article introduces some of the key elements of that debate and traces the appeal and adverse impact of this methodology to the P value fallacy, the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result.
Is the p-value the false positive rate?
False positives A positive is a significant result, i.e. the p-value is less than your cut off value, normally 0.05. A false positive is when you get a significant difference where, in reality, none exists. As I mentioned above, the p-value is the chance that this data could occur given no difference actually exists.
Why is my p value so high?
High p-values indicate that your evidence is not strong enough to suggest an effect exists in the population. An effect might exist but it’s possible that the effect size is too small, the sample size is too small, or there is too much variability for the hypothesis test to detect it.
What does P value of 0.04 mean?
In this context, what P = 0.04 (i.e., 4%) means is that if the null hypothesis is true and if you perform the study a large number of times and in exactly the same manner, drawing random samples from the population on each occasion, then, on 4% of occasions, you would get the same or greater difference between groups …
How is the p value of a statistic calculated?
A p value is calculated under the assumption that the medication does not work and tells us the probability of obtaining the data we did, or data more extreme than it. It does not tell us the chance the medication is effective.
Why is the base rate of false positives so low?
In statistical terms, my false discovery rate – the fraction of statistically significant results which are really false positives – is 38%. Because the base rate of effective cancer drugs is so low – only 10% of our hundred trial drugs actually work – most of the tested drugs do not work, and we have many opportunities for false positives.
When is the base rate paradox counter intuitive?
The paradox surprises most people. It is especially counter-intuitive when interpreting a positive result in a test on a low-prevalence population after having dealt with positive results drawn from a high-prevalence population.
What is the p value of a drug?
Remember that p values are calculated under the assumption of no effect, so p = 0.05 means a 5% chance of falsely concluding that an ineffectual drug works. So I perform my experiments and conclude there are 13 working drugs: 8 good drugs and 5 I’ve included erroneously, shown in red: