Validity (statistics)

Validity is the main extent to which a concept, conclusion or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below. In psychometrics, validity has a particular application known as test validity: "the degree to which evidence and theory support the interpretations of test scores" ("as entailed by proposed uses of tests"). It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical measures and as such is an epistemological and philosophical issue as well as a question of measurement. The use of the term in logic is narrower, relating to the relationship between the premises and conclusion of an argument. In logic, validity refers to the property of an argument whereby if the premises are true then the truth of the conclusion follows by necessity. The conclusion of an argument is true if the argument is sound, which is to say if the argument is valid and its premises are true. By contrast, "scientific or statistical validity" is not a deductive claim that is necessarily truth preserving, but is an inductive claim that remains true or false in an undecided manner. This is why "scientific or statistical validity" is a claim that is qualified as being either strong or weak in its nature, it is never necessary nor certainly true. This has the effect of making claims of "scientific or statistical validity" open to interpretation as to what, in fact, the facts of the matter mean. Validity is important because it can help determine what types of tests to use, and help to make sure researchers are using methods that are not only ethical, and cost-effective, but also a method that truly measures the idea or constructs in question. (Wikipedia).

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

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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This lecturelet will introduce you to the series on statistical analyses of time-frequency data. For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

From playlist OLD ANTS #8) Statistics

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Visit http://ilectureonline.com for more math and science lectures! We will discuss variability: The accuracy of statistical results depend on the (sources of) variability of the collected data. To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 . Next

From playlist STATISTICS CH 1 INTRODUCTION

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From playlist Hypothesis Tests and Critical Values

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From playlist Statistics

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In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.

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Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set

https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set

From playlist Statistics (Full Length Videos)

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From playlist Statistics (Full Length Videos)

Machine Learning using Boosting Regression in JASP free software | Supervised learning

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Statistical Learning: 6.5 Validation and cross validation

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From playlist Statistical Learning

Statistical Learning: 5.2 K-fold Cross Validation

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From playlist Statistical Learning

Ses 2 | MIT Abdul Latif Jameel Poverty Action Lab Executive Training

Session 2: Why randomize? Speaker: Dan Levy See the complete course at: http://ocw.mit.edu/jpal License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT Abdul Latif Jameel Poverty Action Lab Executive Training

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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3notMzh Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html

From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)

Valid discrete probability distribution examples | Random variables | AP Statistics | Khan Academy

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From playlist Random variables | AP Statistics | Khan Academy

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From playlist Statistical Learning

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Validity (statistics)

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