As D.M.C. would say, bad meaning bad not bad meaning good.
Definition: A mathematical technique to measure whether the results of a study are likely to be true. Statistical significance is calculated as the probability that an effect observed in a research study is occurring because of chance. Statistical significance is usually expressed as a P-value. The smaller the P-value, the less likely it is that the results are due to chance (and more likely that the results are true). Researchers generally believe the results are probably true if the statistical significance is a P-value less than 0.05 (p<.05). Example: For example, results from a research study indicated that people who had dementia with agitation had a slightly lower rate of blood pressure problems when they took Drug A compared to when they took Drug B. In the study analysis, these results were not considered to be statistically significant because p=0.2. The probability that the results were due to chance was high enough to conclude that the two drugs probably did not differ in causing blood pressure problems.
The definition is wrong, as is the example. I mean, really wrong. So wrong that it’s perversely impressive how many errors they managed to pack into two brief paragraphs:
1. I don’t even know what it means to say “whether the results of a study are likely to be true.” The results are the results, right? You could try to give them some slack and assume they meant, “whether the results of a study represent a true pattern in the general population” or something like that—but, even so, it’s not clear what is meant by “true.”
2. Even if you could some how get some definition of “likely to be true,” that is not what statistical significance is about. It’s just not.
3. “Statistical significance is calculated as the probability that an effect observed in a research study is occurring because of chance.” Ummm, this is close, if you replace “an effect” with “a difference at least as large as what was observed” and if you append “conditional on there being a zero underlying effect.” Of course in real life there are very few zero underlying effects (I hope the Agency for Healthcare Research and Quality mostly studies treatments with positive effects!), hence the irrelevance of statistical significance to relevant questions in this field.
4. “The smaller the P-value, the less likely it is that the results are due to chance (and more likely that the results are true).” No no no no no. As has been often said, the p-value is a measure of sample size. And, even conditional on sample size, and conditional on measurement error and variation between people, the probability that the results are true (whatever exactly that means) depends strongly on what is being studied, what Tversky and Kahneman called the base rate.
5. As Mayo points out, it’s sloppy to use “likely” to talk about probability.
6. “Researchers generally believe the results are probably true if the statistical significance is a P-value less than 0.05 (p<.05)." Ummmm, yes, I guess that's correct. Lots of ignorant researchers believe this. I suppose that, without this belief, Psychological Science would have difficulty filling its pages, and Science, Nature, and PPNAS would have no social science papers to publish and they'd have to go back to their traditional plan of publishing papers in the biological and physical sciences. 7. "The probability that the results were due to chance was high enough to conclude that the two drugs probably did not differ in causing blood pressure problems." Hahahahahaha. Funny. What's really amusing is that they hyperlink "probability" so we can learn more technical stuff from them. OK, I'll bite, I'll follow the link:
Definition: The likelihood (or chance) that an event will occur. In a clinical research study, it is the number of times a condition or event occurs in a study group divided by the number of people being studied.
Example: For example, a group of adult men who had chest pain when they walked had diagnostic tests to find the cause of the pain. Eighty-five percent were found to have a type of heart disease known as coronary artery disease. The probability of coronary artery disease in men who have chest pain with walking is 85 percent.
Fuuuuuuuuuuuuuuuck. No no no no no. First, of course “likelihood” has a technical use which is not the same as what they say. Second, “the number of times a condition or event occurs in a study group divided by the number of people being studied” is a frequency, not a probability.
It’s refreshing to see these sorts of errors out in the open, though. If someone writing a tutorial makes these huge, huge errors, you can see how everyday researchers make these mistakes too.
A pair of researchers find that, for a certain group of women they are studying, three times as many are wearing red or pink shirts during days 6-14 of their monthly cycle (which the researchers, in their youthful ignorance, were led to believe were the most fertile days of the month). Therefore, the probability (see above definition) of wearing red or pink is three times more likely during these days. And the result is statistically significant (see above definition), so the results are probably true. That pretty much covers it.
All snark aside, I’d never really had a sense of the reasoning by which people get to these sorts of ridiculous claims based on such shaky data. But now I see it. It’s the two steps: (a) the observed frequency is the probability, (b) if p less than .05 then the result is probably real. Plus, the intellectual incentive of having your pet theory confirmed, and the professional incentive of getting published in the tabloids. But underlying all this are the wrong definitions of “probability” and “statistical significance.”
Who wrote these definitions in this U.S. government document, I wonder? I went all over the webpage and couldn’t find any list of authors. This relates to a recurring point made by Basbøll and myself: it’s hard to know what to do with a piece of writing if you don’t know where it came from. Basbøll and I wrote about this in the context of plagiarism (a statistical analogy would be the statement that it can be hard to effectively use a statistical method if the person who wrote it up doesn’t understand it himself), but really the point is more general. If this article on statistical significance had an author of record, we could examine the author’s qualifications, possibly contact him or her, see other things written by the same author, etc. Without this, we’re stuck.
Wikipedia articles typically don’t have named authors, but the authors do have online handles and they thus take responsibility for their words. Also Wikipedia requires sources. There are no sources given for these two paragraphs on statistical significance which are so full of errors.
The question then arises: how should statistical significance be defined in one paragraph for the layperson? I think the solution is, if you’re not gonna be rigorous, don’t fake it.
Here’s my try.
Definition: A mathematical technique to measure the strength of evidence from a single study. Statistical significance is conventionally declared when the p-value is less than 0.05. The p-value is the probability of seeing a result as strong as observed or greater, under the null hypothesis (which is commonly the hypothesis that there is no effect). Thus, the smaller the p-value, the less consistent are the data with the null hypothesis under this measure.
I think that’s better than their definition. Of course, I’m an experienced author of statistics textbooks so I should be able to correctly and concisely define p-values and statistical significance. But . . . the government could’ve asked me to do this for them! I’d’ve done it. It only took me 10 minutes! Would I write the whole glossary for them? Maybe not. But at least they’d have a correct definition of statistical significance.
I guess they can go back now and change it.
Just to be clear, I’m not trying to slag on whoever prepared this document. I’m sure they did the best they could, they just didn’t know any better. It would be as if someone asked me to write a glossary about medicine. The flaw is in whoever commissioned the glossary, to not run it by some expert to check. Or maybe they could’ve just omitted the glossary entirely, as these topics are covered in standard textbooks.
P.S. And whassup with that ugly, ugly logo? It’s the U.S. government. We’re the greatest country on earth. Sure, our health-care system is famously crappy, but can’t we come up with a better logo than this? Christ.
P.P.S. Following Paul Alper’s suggestion, I made my definition more general by removing the phrase, “that the true underlying effect is zero.”
P.P.P.S. The bigger picture, though, is that I don’t think people should be making decisions based on statistical significance in any case. In my ideal world, we’d be defining statistical significance just as a legacy project, so that students can understand outdated reports that might be of historical interest. If you’re gonna define statistical significance, you should do it right, but really I think all this stuff is generally misguided.