Another 50 are commanded to abstain from the delicious stuff. Both groups are weighed before the experiment and then after, and their average weight change is compared. It states there is no difference in the weight loss of the chocolate eaters versus the chocolate abstainers. Rejecting the null is a major hurdle scientists need to clear to prove their hypothesis.
And what is science if not a process of narrowing down explanations? In court, you start off with the assumption that the defendant is innocent. Then you start looking at the evidence: the bloody knife with his fingerprints on it, his history of violence, eyewitness accounts.
As the evidence mounts, that presumption of innocence starts to look naive. At a certain point, jurors get the feeling, beyond a reasonable doubt, that the defendant is not innocent. Null hypothesis testing follows a similar logic: If there are huge and consistent weight differences between the chocolate eaters and chocolate abstainers, the null hypothesis — that there are no weight differences — starts to look silly and you can reject it.
Rejecting the null hypothesis is indirect evidence of an experimental hypothesis. It says nothing about whether your scientific conclusion is correct. Sure, the chocolate eaters may lose some weight. But is it because of the chocolate? Or maybe they felt extra guilty eating candy every day, and they knew they were going to be weighed by strangers wearing lab coats weird! If the p-value is very small, it means the numbers would rarely but not never!
So when the p is small, researchers start to think the null hypothesis looks improbable. So scientists instead pick a threshold where they feel pretty confident that they can reject the null. Ideally, a p of. Not at all. Again: A p-value of less than. Psychology PhD student Kristoffer Magnusson has designed a pretty cool interactive calculator that estimates the probability of obtaining a range of p-values for any given true difference between groups.
I used it to create the following scenario. Yes, this is a nerdy way of putting it. But think of it like this: It means 69 percent of those in the experimental group show results higher than the mean of the control group.
Sure, a few studies on a question should get a p-value of. But more should find lower numbers. The biggest change the paper is advocating for is rhetorical: Results that currently meet the. That's all. Historians of science are always quick to point out that Ronald Fisher, the UK statistician who invented the p-value, never intended it to be the final word on scientific evidence.
Most concretely, it mean labs will need to increase the number of participants in their studies by 70 percent. The increased burden of proof — the proposal authors hope — would nudge labs into adopting other practices science reformers have been calling for, such as data sharing and thinking more long-term about their work.
But a second experiment might. The higher threshold encourages labs to reproduce their own work before submitting to a publication. The proposal has critics. One of them is Daniel Lakens, a psychologist at Eindhoven University of Technology in the Netherlands who organized a rebuttal paper with dozens of authors.
We set the maximum speed a little higher, because then we actually get somewhere a little bit quicker. The same is for science.
Ideally, Lakens says, the level of statistical significance needed to prove a hypothesis depends on how outlandish the hypothesis is. But do you need such stringent criteria for a well-worn idea? The high standards could impede young PhDs with low budgets. Again, a p-value of. A good researcher would know how to follow up and suss out the truth. Ideally, he says, scientists would retrain themselves not to rely on null-hypothesis testing.
In the real world, p-values are a quick and easy tool any scientist can use. And in our real world, p-values still carry a lot of weight in determining what gets published. It would help. Redefining statistical significance is not an ideal solution to the problem of replication. Or come to appreciate a more nuanced way of evaluating results? And the real problem is the culture of science. One young scientist told us, "I feel torn between asking questions that I know will lead to statistical significance and asking questions that matter.
She felt torn because young scientists need publications to get jobs. Under the status quo, in order to get publications, you need statistically significant results. The institutions of science incentivized the behaviors that allowed it to fester. Keep in mind, this is all just a proposal, something to spark debate. To my knowledge, journals are not rushing to change their editorial standards overnight.
Yes, a lot of this is just about tweaking language. Our mission has never been more vital than it is in this moment: to empower through understanding. Financial contributions from our readers are a critical part of supporting our resource-intensive work and help us keep our journalism free for all.
Please consider making a contribution to Vox today to help us keep our work free for all. Cookie banner We use cookies and other tracking technologies to improve your browsing experience on our site, show personalized content and targeted ads, analyze site traffic, and understand where our audiences come from.
By choosing I Accept , you consent to our use of cookies and other tracking technologies. What a nerdy debate about p-values shows about science — and how to fix it. Share this story Share this on Facebook Share this on Twitter Share All sharing options Share All sharing options for: What a nerdy debate about p-values shows about science — and how to fix it.
Reddit Pocket Flipboard Email. There is a lot to unpack and understand here. Which is correct? Perhaps one solution is to simply report the p-value and let the reader come to their own conclusion.
Cautions Regarding Interpretation of P-Values. Many researchers and practitioners now prefer confidence intervals, because they focus on the estimated effect size and how precise the estimate is rather than "Is there an effect? Also note that the meaning of "significant" depends on the audience. To scientists it mean "statistically significant," i.
Many public health researchers and practitioners prefer confidence intervals, since p-values give less information and are often interpreted inappropriately. When reporting results one should provide all three of these. Some Rights Reserved. Date last modified: May 16, Wayne W.
0コメント