Validity (statistics)

Test validity

Test validity is the extent to which a test (such as a chemical, physical, or scholastic test) accurately measures what it is supposed to measure. In the fields of psychological testing and educational testing, "validity refers to the degree to which evidence and theory support the interpretations of test scores entailed by proposed uses of tests". Although classical models divided the concept into various "validities" (such as content validity, criterion validity, and construct validity), the currently dominant view is that validity is a single unitary construct. Validity is generally considered the most important issue in psychological and educational testing because it concerns the meaning placed on test results. Though many textbooks present validity as a static construct, various models of validity have evolved since the first published recommendations for constructing psychological and education tests. These models can be categorized into two primary groups: classical models, which include several types of validity, and modern models, which present validity as a single construct. The modern models reorganize classical "validities" into either "aspects" of validity or "types" of validity-supporting evidence Test validity is often confused with reliability, which refers to the consistency of a measure. Adequate reliability is a prerequisite of validity, but a high reliability does not in any way guarantee that a measure is valid. (Wikipedia).

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History of test validity research

History of test validity research Task-based vs competency-based assessment: https://www.youtube.com/watch?v=LCEfIyxoClQ&list=PLTjlULGD9bNJi1NtMfKjr7umeKdQR9DGO&index=18 Test usefulness: https://www.youtube.com/watch?v=jZFeOaYkVzA&list=PLTjlULGD9bNJi1NtMfKjr7umeKdQR9DGO&index=7

From playlist Learn with Experts

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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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Validity, reliability and accuracy explained

What doe validity, reliability and accuracy mean in experiments? Watch and find out. Support me on Patreon - https://www.patreon.com/HighSchoolPhysicsExplained Find me on facebook - www.facebook.com/HighSchoolPhysicsExplained credit Pendulum animation - PhET Interactive Simulations Unive

From playlist general

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Can You Validate These Emails?

Email Validation is a procedure that verifies if an email address is deliverable and valid. Can you validate these emails?

From playlist Fun

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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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Reliability 1: External reliability and rater reliability and agreement

In this video, I discuss external reliability, inter- and intra-rater reliability, and rater agreement.

From playlist Reliability analysis

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Hypothesis Tests AS Level Maths Statistics Exam Questions 2

AS Level Maths Statistics Exam Questions on hypothesis tests with binomial distribution, from AQA, Edexcel and OCR MEI, perfect revision and practice for your AS Maths exams and A Level Maths year 1! Statistical hypothesis testing is a huge part of statistics in A Level Maths, and you'll

From playlist Hypothesis Tests AS Level Maths Statistics Exam Questions

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Selecting the best model in scikit-learn using cross-validation

In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting features. We'll compare cross-validation with the train/test split procedure, and we'll also discuss some variations of cross-vali

From playlist Machine learning in Python with scikit-learn

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Lecture 06-01 Advice for applying machine learning

Machine Learning by Andrew Ng [Coursera] 0601 Deciding what to try next 0602 Evaluating a hypothesis 0603 Model selection and training/validation/test sets 0604 Diagnosing bias vs variance 0605 Regularization and bias/variance 0606 Learning curves 0607 Deciding what to try next (revisited

From playlist Machine Learning by Professor Andrew Ng

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Test Usefulness Argument

In this videos, I have discussed six facets of test usefulness (Bachman & Palmer, 1996) as follows: 1. Practicality 2. Reliability 3. Authenticity 4. Interactiveness 5. Construct Validity 6. Impact

From playlist Language Assessment & Technology

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Rails Conf 2013 TDD Workshop: Mocking, Stubbing, and Faking External Services

By Harlow Ward & Adarsh Pandit Help us caption & translate this video! http://amara.org/v/FGao/

From playlist Rails Conf 2013

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Applied ML 2020 - 03 Supervised learning and model validation

Class materials: https://www.cs.columbia.edu/~amueller/comsw4995s20/

From playlist Applied Machine Learning 2020

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Cross Validation

In this video, we learn a hack to increase the size of our training set while still being able to do validation: cross validation. Link to my notes on Introduction to Data Science: https://github.com/knathanieltucker/data-science-foundations Try answering these comprehension questions to

From playlist Introduction to Data Science - Foundations

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Lecture 0603 Model selection and training/validation/test sets

Machine Learning by Andrew Ng [Coursera] 06-01 Advice for applying machine learning

From playlist Machine Learning by Professor Andrew Ng

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What is a Null Hypothesis?

Overview of null hypothesis, examples of null and alternate hypotheses, and how to write a null hypothesis statement.

From playlist Hypothesis Tests and Critical Values

Related pages

Predictive validity | Construct validity | Correlation | Concurrent validity | Content validity | Reliability (statistics) | Criterion validity