Multiple comparisons | Statistical hypothesis testing

Testing hypotheses suggested by the data

In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set; therefore we hypothesize that it is true in general; therefore we wrongly test it on the same, limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing (from Latin post hoc, "after this"). The correct procedure is to test any hypothesis on a data set that was not used to generate the hypothesis. (Wikipedia).

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Teach Astronomy - Testing a Hypothesis

http://www.teachastronomy.com/ One of the basic tasks of science is to test hypotheses. A hypothesis is a description of a set of data, a model, usually a mathematical description in most branches of science. To test a hypothesis we need data of sufficient quantity and quality, and our a

From playlist 01. Fundamentals of Science and Astronomy

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Hypothesis testing in statistics

Hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter or a population probability distribution. First, a tentative assumption is made about the parameter or distribution. This assumption is called the null hypoth

From playlist Statistics

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Hypotheses

Medical research starts by stating hypothesis. This is done right after the research question has been formulated and precedes any data capture or analysis. It is at the heart of inferential statistics and sets up our view of p-values.

From playlist Learning medical statistics with python and Jupyter notebooks

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Lesson: Null and Alternative Hypotheses

This video introduces null and alternative hypotheses.

From playlist Hypothesis Testing with One Sample

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Null and Alternative Hypotheses in Statistics (7-8)

There are two types of hypotheses we need for hypothesis testing: the one we test (the null hypothesis) and the one that use instead if we reject the null hypothesis (the alternative hypothesis). The null hypothesis (H0) states there is no difference between the experimental (sample) mean

From playlist WK7 Sampling, Probability, & Inference - Online Statistics for the Flipped Classroom

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Intro to Hypothesis Testing

What is a hypothesis test? The meaning of the null and alternate hypothesis, with examples. Overview of test statistics and confidence levels.

From playlist Hypothesis Tests and Critical Values

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A Gentle Introduction to the One Sample z Test (9-7)

Now it is time for our first real inference test (i.e. “hypothesis test”). We will use a one-sample z test to determine whether a sample mean is significantly different than the population mean when the standard deviation of the population is known. I use an example about the average age o

From playlist WK9 Using z Scores and the z Test in Statistics - Online Statistics for the Flipped Classroom

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Introduction to Detection Theory (Hypothesis Testing)

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Includes definitions of binary and m-ary tests, simple and composite hypotheses, decision regions, and test performance characterization: prob

From playlist Estimation and Detection Theory

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Hypothesis Test: Matched or Paired Data

This video explains how to conduct a hypothesis test on paired or matched data.

From playlist Hypothesis Test with Two Samples

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Alternative Hypotheses: Main Ideas!!!

In Statistics, when we do Hypothesis Testing, we are supposed to have two hypotheses: A primary, or Null Hypothesis and an Alternative Hypothesis. This StatQuest explains why we need the Alternative Hypothesis, even though Hypothesis Testing tends to focus on the Null. NOTE: This StatQues

From playlist StatQuest

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JASP 0.14 Tutorial: Learn Bayes Module PART 2 (Binomial Test) (Episode 25)

In this JASP tutorial, I learn how to understand Bayesian statistical methods using the "Learn Bayes" module, available in the latest version of JASP! I did not do Bayesian stats in graduate school, and most frequently use classical/frequentist statistical methods. In "part two" of my two-

From playlist JASP Tutorials

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Do ImageNet Classifiers Generalize to ImageNet? (Paper Explained)

Has the world overfitted to ImageNet? What if we collect another dataset in exactly the same fashion? This paper gives a surprising answer! Paper: https://arxiv.org/abs/1902.10811 Data: https://github.com/modestyachts/ImageNetV2 Abstract: We build new test sets for the CIFAR-10 and Image

From playlist Papers Explained

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HSC Science Extension Module 1 Falsification

HSC Science Extension Module 1 Foundations of Scientific Thinking Falsification Karl Popper

From playlist Y12 Sci Ex Mod 1 Foundations of Scientific Thinking

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Statistics - 10.1.2 Writing Hypotheses

The focus of this video is on determining the direction of the alternative hypothesis in hypothesis testing. Power Point: https://bellevueuniversity-my.sharepoint.com/:p:/g/personal/kbrehm_bellevue_edu/EZD6vROKJFxNr57G87LJtAUBf7XsqWw7RDYpDSwYxqMJfw?e=xOX1Dw This playlist follows the text

From playlist Applied Statistics (Entire Course)

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Constructing hypotheses for a significance test about a proportion | AP Statistics | Khan Academy

Constructing hypotheses for a significance test about a proportion. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/tests-significance-ap/one-sample-z-test-proportion/v/constructing-hypotheses-for-a-significance-test?utm_source=youtube&utm_mediu

From playlist Significance tests (hypothesis testing) | AP Statistics | Khan Academy

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HSC Science Extension Module 1 Induction and Deduction

HSC Science Extension Module 1 Foundations of Scientific Thinking Induction and Deduction

From playlist Y12 Sci Ex Mod 1 Foundations of Scientific Thinking

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Kaggle Reading Group: Deep Learning for Symbolic Mathematics (Part 2) | Kaggle

This week we'll continue with "Deep Learning for Symbolic Mathematics", (anonymous, submitted to ICLR 2020). You can find a link to the paper here: https://openreview.net/forum?id=S1eZYeHFDS SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_... About Kaggle: Kaggle is the world's largest c

From playlist Kaggle Reading Group | Kaggle

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Kaggle Reading Group: On NMT Search Errors and Model Errors: Cat Got Your Tongue? | Kaggle

This week we'll be starting a new paper: "On NMT Search Errors and Model Errors: Cat Got Your Tongue?" by Felix Stahlber and Bill Byrne, published at EMNLP 2019. You can follow along with the paper here: https://www.aclweb.org/anthology/D19-1331.pdf About Kaggle: Kaggle is the world's lar

From playlist Kaggle Reading Group | Kaggle

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How To Stop Worrying and Learn to Love Qualitative Data - Farrah Bostic keynote

From Strata + Hadoop World 2015 in Singapore: Most organizations are doing qualitative research badly, and then blaming the research participants. They also discount the utility of qualitative approaches in innovation – but qualitative data may be your best bet for discovering new opportun

From playlist Strata + Hadoop World - Singapore 2015

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Statistics: Ch 9 Hypothesis Testing (4 of 35) What is the "Alternative Hypothesis"?

Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 The null hypothesis (H0) is a statement about the population, the product, the design, of the capability or property of a population,

From playlist STATISTICS CH 9 HYPOTHESIS TESTING

Related pages

Statistical inference | Data dredging | Post hoc analysis | Type I and type II errors | Uncomfortable science | Publication bias | Cross-validation (statistics) | Overfitting | Analysis of variance | Predictive analytics | Exploratory data analysis | Probability | Statistics | Statistical model | Statistical hypothesis testing | Bonferroni correction | Data mining