Reddit - Dive into anything. The American Statistical Association's Statement on the Use of P Values. P values have been around for nearly a century and they’ve been the subject of criticism since their origins. In recent years, the debate over P values has risen to a fever pitch. In particular, there are serious fears that P values are misused to such an extent that it has actually damaged science. In March 2016, spurred on by the growing concerns, the American Statistical Association (ASA) did something that it has never done before and took an official position on a statistical practice—how to use P values. The ASA tapped a group of 20 experts who discussed this over the course of many months. Despite facing complex issues and many heated disagreements, this group managed to reach a consensus on specific points and produce the ASA Statement on Statistical Significance and P-values.
I’ve written previously about my concerns over how P values have been misused and misinterpreted. I discuss these ideas in my post How to Correctly Interpret P Values. The Practical Alternative to the p Value Is the Correctly Used p Value. Because of the strong overreliance on p values in the scientific literature, some researchers have argued that we need to move beyond p values and embrace practical alternatives. When proposing alternatives to p values statisticians often commit the "statistician's fallacy," whereby they declare which statistic researchers really "want to know. " Instead of telling researchers what they want to know, statisticians should teach researchers which questions they can ask. In some situations, the answer to the question they are most interested in will be the p value. As long as null-hypothesis tests have been criticized, researchers have suggested including minimum-effect tests and equivalence tests in our statistical toolbox, and these tests have the potential to greatly improve the questions researchers ask.
Keywords: equivalence tests; null-hypothesis testing; p values; statistical inferences. Nobody understands p-values. Reddit - Dive into anything. Understanding Statistical Power and Significance Testing. There are two main reasons why frequentist[1] methods are losing popularity: 1. Your tests make statements about some pre-specified null hypothesis, not the actual question you’re usually interested in: Does this model fit my data? 2. The probability statements from frequentist methods are usually about the properties of the estimator, not about the thing you’re estimating. This is very unintuitive and confusing. So why are these big deals? The easiest way to think about it is imagine you have some test that you’ve designed that tests if a person’s height is 5’ 10”. The problem with this approach is only a problem if you’re situation doesn’t match the conditions the test was originally designed for.
If we look back at the story of the height test, then you can already see the beginnings of our second point. Isn’t that weird? This is the exact problem with confidence intervals and p-values (which are basically two ways of talking about the same thing). Reddit - Dive into anything. Reddit - Dive into anything.
Reddit - Dive into anything. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations - PMC. How many False Discoveries are Published in Psychology? | Replicability-Index. For decades psychologists have ignored statistics because the only knowledge required was that p-values less than .05 can be published and p-values greater than .05 cannot be published.
Hence, psychologists used statistics programs to hunt for significant results without understanding the meaning of statistical significance. Since 2011, psychologists are increasingly recognizing that publishing only significant results is a problem (cf. Sterling, 1959). However, psychologists are confused what to do instead. First, it doesn’t require a degree in math to understand what p < .05 means. That is, if a significant correlation in a sample is positive, the probability that the correlation in the population is zero or negative is at most 5%.
This is quiet obvious when we look at probabilities in terms of long-run frequencies. Importantly, this is not an empirical observation. It would be easy to answer this question if all hypothesis tests were published (Sterling, 1959). Implications Like this: Estimating the evidential value of significant results in psychological science | PLOS ONE. Abstract Quantifying evidence is an inherent aim of empirical science, yet the customary statistical methods in psychology do not communicate the degree to which the collected data serve as evidence for the tested hypothesis. In order to estimate the distribution of the strength of evidence that individual significant results offer in psychology, we calculated Bayes factors (BF) for 287,424 findings of 35,515 articles published in 293 psychological journals between 1985 and 2016. Overall, 55% of all analyzed results were found to provide BF > 10 (often labeled as strong evidence) for the alternative hypothesis, while more than half of the remaining results do not pass the level of BF = 3 (labeled as anecdotal evidence).
The results estimate that at least 82% of all published psychological articles contain one or more significant results that do not provide BF > 10 for the hypothesis. Editor: Jelte M. Wicherts, Tilburg University, NETHERLANDS Copyright: © 2017 Aczel et al. Introduction. “The 2019 ASA Guide to P-values and Statistical Significance: Don’t Say What You Don’t Mean” (Some Recommendations)(ii) | Error Statistics Philosophy. Some have asked me why I haven’t blogged on the recent follow-up to the ASA Statement on P-Values and Statistical Significance (Wasserstein and Lazar 2016)–hereafter, ASA I. They’re referring to the editorial by Wasserstein, R., Schirm, A. and Lazar, N. (2019)–hereafter, ASA II(note)–opening a special on-line issue of over 40 contributions responding to the call to describe “a world beyond P < 0.05”.[1] Am I falling down on the job? Not really. All of the issues are thoroughly visited in my Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars, SIST (2018, CUP).
I invite interested readers to join me on the statistical cruise therein.[2] As the ASA II(note) authors observe: “At times in this editorial and the papers you’ll hear deep dissonance, the echoes of ‘statistics wars’ still simmering today (Mayo 2018)”. So I should say something. The Invitation to Broader Consideration and Debate Excellent! (Wow!) What’s the Relationship Between ASA I and ASA II(note)? Schachtman Law » Has the American Statistical Association Gone Post-Modern?
Last week, the American Statistical Association (ASA) released a special issue of its journal, The American Statistician, with 43 articles addressing the issue of “statistical significance.” If you are on the ASA’s mailing list, you received an email announcing that “the lead editorial calls for abandoning the use of ‘statistically significant’, and offers much (not just one thing) to replace it.
Written by Ron Wasserstein, Allen Schirm, and Nicole Lazar, the co-editors of the special issue, ‘Moving to a World Beyond ‘p < 0.05’ summarizes the content of the issue’s 43 articles.” In 2016, the ASA issued its “consensus” statement on statistical significance, in which it articulated six principles for interpreting p-values, and for avoiding erroneous interpretations. Ronald L. Wasserstein & Nicole A. According to the lead editorial for the special issue: The ASA (through Wasserstein and colleagues) appear to be condemning dichotomizing p-values, which are a continuum between zero and one.
The Statistical Crisis in Science. This Article From Issue November-December 2014 Volume 102, Number 6 View Issue There is a growing realization that reported “statistically significant” claims in scientific publications are routinely mistaken. Researchers typically express the confidence in their data in terms of p-value: the probability that a perceived result is actually the result of random variation. The idea is that when p is less than some prespecified value such as 0.05, the null hypothesis is rejected by the data, allowing researchers to claim strong evidence in favor of the alternative.
In general, p-values are based on what would have happened under other possible data sets. At this point a huge number of possible comparisons could be performed, all consistent with the researcher’s theory. We might see a difference between the sexes in the healthcare context but not the military context; this would make sense given that health care is currently a highly politically salient issue and the military is less so. A letter in response to the ASA’s Statement on p-Values by Ionides, Giessing, Ritov and Page | Error Statistics Philosophy. I came across an interesting letter in response to the ASA’s Statement on p-values that I hadn’t seen before.
It’s by Ionides, Giessing, Ritov and Page, and it’s very much worth reading. I make some comments below. Edward L. Ionidesa, Alexander Giessinga, Yaacov Ritova, and Scott E. PagebaDepartment of Statistics, University of Michigan, Ann Arbor, MI;bDepartments of Complex Systems, Political Science and Economics, University of Michigan, Ann Arbor, MIThe ASA’s statement on p-values: context, process, and purpose (Wasserstein and Lazar 2016) makes several reasonable practical points on the use of p-values in empirical scientific inquiry. 2017, Vol. 71, No.1 1.
In my view, the strongest reason that a reader of the Guide would view it as recommending against frequentist methods is its unclarity regarding principle 4, in favor of full reporting and transparency. 2. In deductive reasoning all knowledge obtainable is already latent in the postulates. We get what I call “lift off”. The American Statistical Association statement on P-values explained - PMC. Demystifying the New Statistical Recommendations | JACC: Journal of the American College of Cardiology. Why I've lost faith in p values — Luck Lab. This might not seem so bad. I'm still drawing the right conclusion over 90% of the time when I get a significant effect (assuming that I've done everything appropriately in running and analyzing my experiments). However, there are many cases where I am testing bold, risky hypotheses—that is, hypotheses that are unlikely to be true. As Table 2 shows, if there is a true effect in only 10% of the experiments I run, almost half of my significant effects will be bogus (i.e., p(null | significant effect) = .47).
The probability of a bogus effect is also high if I run an experiment with low power. For example, if the null and alternative are equally likely to be true (as in Table 1), but my power to detect an effect (when an effect is present) is only .1, fully 1/3 of my significant effects would be expected to be bogus (i.e., p(null | significant effect) = .33). Yesterday, one of my postdocs showed me a small but statistically significant effect that seemed unlikely to be true. So you banned p-values, how’s that working out for you? The journal Basic and Applied Social Psychology banned p-values a year ago. I read some of their articles published in the last year. I didn’t like many of them. Here’s why. First of all, it seems BASP didn’t just ban p-values. They also banned confidence intervals, because God forbid you use that lower bound to check whether or not it includes 0.
It reminds me of alcoholics who go into detox and have to hand in their perfume, before they are tempted to drink it. There are some nice papers where the p-value ban has no negative consequences. But in many other papers, especially those where sample sizes were small, and experimental designs were used to examine hypothesized differences between conditions, things don’t look good. In many of the articles published in BASP, researchers make statements about differences between groups. Saying one thing is bigger than something else, and reporting an effect size, works pretty well in simple effects. Scientists Replicated 100 Psychology Studies, and Fewer Than Half Got the Same Results | Science| Smithsonian Magazine. Academic journals and the press regularly serve up fresh helpings of fascinating psychological research findings.
But how many of those experiments would produce the same results a second time around? According to work presented today in Science, fewer than half of 100 studies published in 2008 in three top psychology journals could be replicated successfully. The international effort included 270 scientists who re-ran other people's studies as part of The Reproducibility Project: Psychology, led by Brian Nosek of the University of Virginia.
The eye-opening results don't necessarily mean that those original findings were incorrect or that the scientific process is flawed. When one study finds an effect that a second study can't replicate, there are several possible reasons, says co-author Cody Christopherson of Southern Oregon University. “This project is not evidence that anything is broken. Rather, it's an example of science doing what science does,” says Christopherson. Statisticians Found One Thing They Can Agree On: It’s Time To Stop Misusing P-Values. Do multiple outcome measures require p-value adjustment? - PMC. Not Even Scientists Can Easily Explain P-values. Handbook of Biological Statistics has moved! BMC Medical Research Methodology | Full text | How confidence intervals become confusion intervals.
Most published reports of clinical studies begin with an abstract – likely the first and perhaps only thing many clinicians, the media and patients will read. Within that abstract, authors/investigators typically provide a brief summary of the results and a 1–2 sentence conclusion. At times, the conclusion of one study will be different, even diametrically opposed, to another despite the authors looking at similar data. In these cases, readers may assume that these individual authors somehow found dramatically different results. While these reported differences may be true some of the time, radically diverse conclusions and ensuing controversies may simply be due to tiny differences in confidence intervals combined with an over-reliance and misunderstanding of a “statistically significant difference.”
Unfortunately, this misunderstanding can lead to therapeutic uncertainty for front-line clinicians when in fact the overall data on a particular issue is remarkably consistent. Statins. Introduction to Probability and Statistics. Calculation and Chance Most experimental searches for paranormal phenomena are statistical in nature. A subject repeatedly attempts a task with a known probability of success due to chance, then the number of actual successes is compared to the chance expectation. If a subject scores consistently higher or lower than the chance expectation after a large number of attempts, one can calculate the probability of such a score due purely to chance, and then argue, if the chance probability is sufficiently small, that the results are evidence for the existence of some mechanism (precognition, telepathy, psychokinesis, cheating, etc.) which allowed the subject to perform better than chance would seem to permit.
Suppose you ask a subject to guess, before it is flipped, whether a coin will land with heads or tails up. But suppose this subject continues to guess about 60 right out of a hundred, so that after ten runs of 100 tosses—1000 tosses in all, the subject has made 600 correct guesses. Books. FAQ 1317 - Common misunderstandings about P values. Kline (see book listing below) lists commonly believed fallacies about P values, which I summarize here: Fallacy: P value is the probability that the result was due to sampling error The P value is computed assuming the null hypothesis is true.
In other words, the P value is computed based on the assumption that the difference was due to sampling error. Therefore the P value cannot tell you the probability that the result is due to sampling error. Fallacy: The P value Is the probability that the null hypothesis is true Nope. The P value is computed assuming that the null hypothesis is true, so cannot be the probability that it is true. Fallacy: 1-P is the probability that the alternative hypothesis is true If the P value is 0.03, it is very tempting to think: If there is only a 3% probability that my difference would have been caused by random chance, then there must be a 97% probability that it was caused by a real difference.
But this is wrong! The P value and α are not the same. Note on p values. Misinterpretations of p-values. STAT 101: In-class problems on hypothesis tests. Definition. P-Value -- from Wolfram MathWorld. P Values (Calculated Probability) and Hypothesis Testing - StatsDirect. Pvalue. Type I and II error. Type I and II Errors. Statistics Glossary - hypothesis testing. Lecture FDR. What is confidence? Part 1: The use and interp... [Ann Emerg Med. 1997. The (mis)use of overlap of confidence intervals to assess effect modification. Worldwide Confusion, P-values vs Error Probability. Stat Significance, p-val.
P-value confusion.