Statistical hypothesis testing | Probability fallacies
Misuse of p-values is common in scientific research and scientific education. p-values are often used or interpreted incorrectly; the American Statistical Association states that p-values can indicate how incompatible the data are with a specified statistical model. From a Neyman–Pearson hypothesis testing approach to statistical inferences, the data obtained by comparing the p-value to a significance level will yield one of two results: either the null hypothesis is rejected (which however does not prove that the null hypothesis is false), or the null hypothesis cannot be rejected at that significance level (which however does not prove that the null hypothesis is true). From a Fisherian statistical testing approach to statistical inferences, a low p-value means either that the null hypothesis is true and a highly improbable event has occurred or that the null hypothesis is false. (Wikipedia).
p values – A Technical Deep Dive
Target audience: scientists who read or write studies containing p-values. Created for 3blue1brown’s Summer of Math Exploration 1.
From playlist Summer of Math Exposition Youtube Videos
p-hacking: What it is and how to avoid it!
p-hacking is the misuse and abuse of p-values and results in being fooled by false positives. Some forms of p-hacking are obvious, but other are much more subtle. In this video, we talk about two forms of p-hacking and how to avoid them. NOTE: This StatQuest assumes that you are already f
From playlist StatQuest
PValues.2.pvalues have a uniform dist when null is true
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist P-values: interpretation and distribution
Statistics Lecture 8.2 Part 11
Statistics Lecture 8.2 Part 11: An Introduction to Hypothesis Testing
From playlist Statistics Playlist 1
PValues.1.pvalue less than cutoff if null is true
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist P-values: interpretation and distribution
p-values: What they are and how to interpret them
This StatQuest is all about interpreting p-values. You've seen them online or in publications, or heard about them, whispered in dark, rave filled dance clubs, but you've never understood what they were all about. This 'Quest is here to explain everything you wanted to know about how to in
From playlist StatQuest
A bonus video to my series on medical statistics. Why doing research in private practice is important and understanding the p-value in medical statistics.
From playlist Medical Statistics
The Great Recession | International Economic Institutions | The Great Courses
The Great Recession was like a liquor tumbler of misused financial tools, misapplied risk models, interest rate mistakes, and bad government guidance on credit policy—and we all got dragged to the bar for shots. Line up for a hotly-debated risk management question of the tool versus its us
From playlist Latest Uploads
A selective survey of selective inference – Jonathan Taylor – ICM2018
Probability and Statistics Invited Lecture 12.9 A selective survey of selective inference Jonathan Taylor Abstract: It is not difficult to find stories of a crisis in modern science, either in the popular press or in the scientific literature. There are likely multiple sources for this c
From playlist Probability and Statistics
The most famous equation in all of science is Einstein’s E = mc2, but it is also frequently horribly misunderstood and misused. In this video, Fermilab’s Dr. Don Lincoln explains the real truth about this equation and how people often use it wrong. Related videos: www.youtube.com/watch?v
From playlist Videos by Don Lincoln
The method that can "prove" almost anything - James A. Smith
Explore the data analysis method known as p-hacking, where data is misrepresented as statistically significant. -- In 2011, a group of researchers conducted a study designed to find an impossible result. Their study involved real people, truthfully reported data, and commonplace statisti
From playlist New TED-Ed Originals
Lecture 19 | The Fourier Transforms and its Applications
Lecture by Professor Brad Osgood for the Electrical Engineering course, The Fourier Transforms and its Applications (EE 261). Professor Osgood demonstrates aliasing by showing the class what happens when you under sample music. The Fourier transform is a tool for solving physical probl
From playlist Lecture Collection | The Fourier Transforms and Its Applications
22C3: Anonymous Data Broadcasting by Misuse of Satellite ISPs
Speaker: Sven Löschner An open-source project to develop a tool for broadband satellite broadcasts The lecture focuses on satellite ISP technology and how to misuse it for anonymously broadcasting to an unlimited number of anonymous users while only one user pays for a standard dial-up c
From playlist 22C3: Private Investigations
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers a wide variety of fit indices, their theoretical backgrounds, suggested scores, and some formulae for various indices. Lecture materials and assignment available at statisticsofdoom.com. https://sta
From playlist Structural Equation Modeling
P-Value Method For Hypothesis Testing
This statistics video explains how to use the p-value to solve problems associated with hypothesis testing. When the p-value is less than alpha, you should reject the null hypothesis and vice versa. This video discusses when you should use a one tailed test compared to a two tailed test.
From playlist Statistics
How the F Statistic is used in ANOVA and Regression. How the p-value needs to be considered along with an F-value in your test results.
From playlist Hypothesis Tests and Critical Values
Learning to Represent Programs with Graphs | TDLS
Toronto Deep Learning Series, 25 June 2018 For slides and more information, visit https://tdls.a-i.science/events/2018-06-25/ Paper Review: https://arxiv.org/abs/1711.00740 Speaker: https://www.linkedin.com/in/amirfz/ Organizer: https://www.linkedin.com/in/amirfz/ Host: http://www.rbc.
From playlist Graph Neural Networks