EndOfSignificance. Misunderstood confidence. Do multiple outcome measures require p-value adjustment? Not Even Scientists Can Easily Explain P-values. Problem of alpha inflation. PSY6003 Multiple regression: Revision/Introduction. PSY6003 Advanced statistics: Multivariate analysis II: Manifest variables analyses Contents of this handout: What is multiple regression, where does it fit in, and what is it good for?
The idea of a regression equation; From simple regression to multiple regression; interpreting and reporting multiple regression results; Carrying out multiple regression; Exercises; Worked examples using Minitab and SPSS These notes cover the material of the first lecture, which is designed to remind you briefly of the main ideas in multiple regression. They are not full explanations; they assume you have at least met multiple regression before. If you haven't, you will probably need to read Bryman & Cramer, pp. 177-186 and pp. 235-246. Hypothesis testing. 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. 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. 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. Pvalue.pdf. Note_on_p_values. Misinterpret P-value. Misinterpretations of p-values. Misinterpret p-values and hypothesis tests. Statistics 101 Data Analysis and Statistical Inference In-class problems on hypothesis tests Conceptual questions on hypothesis testing Decide whether the following statements are true or false.
Explain your reasoning. Problems: P-value Definition - short. P-Value WolframMath. P values, stats-direct. PAGE RETIRED: Click here for the new StatsDirect help system.
What is a Pvalue? Wordy. P-value theory. Tools for Teaching and Assessing Statistical Inference. Type I and II error. Type I and II Errors. COMMON MISTEAKS MISTAKES IN USING STATISTICS: Spotting and Avoiding Them Introduction Types of Mistakes Suggestions Resources Table of Contents About Type I and II Errors and Significance Levels Type I Error.
Statistics Glossary - hypothesis testing. Hypothesis Test Setting up and testing hypotheses is an essential part of statistical inference.
In order to formulate such a test, usually some theory has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved, for example, claiming that a new drug is better than the current drug for treatment of the same symptoms. In each problem considered, the question of interest is simplified into two competing claims / hypotheses between which we have a choice; the null hypothesis, denoted H0, against the alternative hypothesis, denoted H1.
These two competing claims / hypotheses are not however treated on an equal basis: special consideration is given to the null hypothesis. We have two common situations: Type 1 & II Errors + other ideas. 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. Fisher vs Neyman-Pearson - dag.pdf. Stat Significance, p-val. An observed positive or negative correlation may arise from purely random effects.
Statistical significance testing methodology gives a way of determining whether an observed correlation is just because of random occurrences, or whether it is a real phenomenon, i.e., statistically significant. The ingredients of statistical significance testing are given by the null hypothesis and the test statistic. The null hypothesis describes the case when there is no correlation. Graphpad p-value confusion.
Most of the multiple comparisons tests report 95% confidence intervals for the difference between means, and also reports which of those comparisons are statistically significant after accounting for the number of comparisons.
Many scientists want more. They ask us to report "exact P values" from multiple comparisons. The quick answer is: No, Prism cannot do that yet. While this sounds like a simple request, in fact it can only be understood by discussing some fundamental, almost philosophical, questions. Statistical principles.