Search results “What is a statistical power analysis”

This video is the first in a series of videos related to the basics of power analyses.
All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/

Views: 13663
Center for Open Science

Introduction to power in significance tests.
View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/v/introduction-to-power-in-significance-tests?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics
AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics.
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Views: 37677
Khan Academy

There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship.
For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?"
The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out."
Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout."
The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary.
In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables.
Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis.
A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one.
A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it.
To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred.
A type II error happens when you decide your prediction is wrong when you are actually right.
One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules.
It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study.
When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level.
Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists.
The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...

Views: 95627
NurseKillam

Learn the basic concepts of power and sample size calculations. With definitions for alpha levels and statistical power and effect size, a brief look at Stata's interface, and strategies for increasing statistical power, this video is a useful introduction for all subsequent power and sample size videos on the Stata Youtube Channel. Created using Stata 13; new features available in Stata 14. Copyright 2011-2017 StataCorp LLC. All rights reserved.

Views: 62123
StataCorp LLC

Definition of power, Type II errors, and sample size issues

Views: 55460
Keith Bower

This video present an example problem for finding the power of an experimental design.

Views: 7710
Matthew Novak

This video covers the types of errors you can commit when making conclusions about populations based on sample data (Type I and Type II errors), p-values, statistical power, and power analysis.
To see an example of a power analysis for a study for which the data are analyzed using a two-sample t-test, check out my blog: https://www.biostatisticsbydesign.com/blog/2019/1/11/power-analysis-an-underutilized-tool
If you'd like to contact me for a statistics consultation, fill out a request form here: https://www.biostatisticsbydesign.com/request-a-consultation/
Visit my website https://www.biostatisticsbydesign.com

Views: 105
Biostatistics By Design

If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at [email protected] indicating your university affiliation and which videos you've found useful.
Thank you! - Dr. Julian Parris
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Tutorial on Visualizing and Calculating Statistical Power for simple hypothesis testing using z-tests.

Views: 38944
ProfessorParris

This video explains what statistical power is. Power = the probability of rejecting the null hypothesis when it is false.
Click here for free access to all of our videos: https://www.youtube.com/user/statisticsinstructor
(Remember to click on "Subscribe")
Power
Type I error
Type II error
Hypothesis testing in statistics

Views: 5385
Quantitative Specialists

Illustration of how statistical power works, and how it can increase or decrease.
Related blog post: http://www.andysbrainblog.blogspot.com/2013/02/the-will-to-fmri-power.html

Views: 16354
Andrew Jahn

http://thedoctoraljourney.com/ This tutorial focuses on the power of a statistical procedure and how power is maximized.
For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.

Views: 9502
The Doctoral Journey

Power and Sample Size Calculation
Motivation and Concepts of Power/Sample Calculation, Calculating Power and Sample Size Using Formula, Software, and Power Chart

Views: 14660
Kunchok Dorjee

This video describes how you can use an online calculator to figure out how big your cell sizes should be for an experiment. The video uses SPSS to help determine the mean & standard deviation for your dependent variables. The online calculator completes the power analysis to show required cell size. The calculator used in this video is: https://www.statisticalsolutions.net/pssZtest_calc.php

Views: 1990
Kathleen Sweetser

Views: 20497
Elizabeth Lynch

A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.

Views: 259728
jbstatistics

This video explains how to calculate a priori and post hoc power calculations for correlations and t-tests using G*Power.
G*Power download: http://www.gpower.hhu.de/en.html
Howell reference: Howell, D. C. (2012). Statistical methods for psychology. Cengage Learning.

Views: 23681
Social Science Club

This lecture discusses utilizing power analysis in experimental design.

Views: 4630
Matthew Novak

Video providing an overview of how power is determined and how it relates to sample size.

Views: 48372
Terry Shaneyfelt

An example of calculating power and the probability of a Type II error (beta), in the context of a Z test for one mean. Much of the underlying logic holds for other types of tests as well.
If you are looking for an example involving a two-tailed test, I have a video with an example of calculating power and the probability of a Type II error for a two-tailed Z test at http://youtu.be/NbeHZp23ubs.

Views: 307237
jbstatistics

This video will go over three issues that can arise when scientific studies have low statistical power.
All materials shown in the video, as well as the content from our other videos, can be found here: https://osf.io/7gqsi/

Views: 2706
Center for Open Science

To view a playlist and download materials shown in this eCourse, visit the course page at: http://www.jmp.com/en_us/academic/ssms.html

Views: 13843
ProfessorParris

How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given information (Ho/Ha, n, sigma, and alpha). At the 8 minute mark, I introduce the alternative mu of 20.5 (a hypothetical value, as are most alternative values of mu, to calculate the power of the test against this alternative). This is a "two sided, greater than" example.
A "one sided, less than" example can be found here:
http://www.youtube.com/watch?v=zXbSogwX8Wc
Stoney Pryor

Views: 83788
StoneyP94

Who:
Dr. Daniël Lakens
Assistant Professor of Psychology
Eindhoven University of Technology
Questions:
- What is "power"?
- Why is it important to consider power and sample size before designing a study?
- What effect does a lack of consideration of power and sample size have on knowledge in the field?

Views: 5009
Society for Personality and Social Psychology

What is a power analysis and when should we do it when scheduling a clinic study or other experimental design? Do we always need one?

Views: 7359
FredDoreyStatistics

This video demonstrates how to calculate power and the probability of Type II error (beta error) in SPSS. Observed power and its relationship to beta error probability are reviewed.

Views: 22552
Dr. Todd Grande

How to calculate sample sizes for t-tests (independent and paired samples)
Download G*Power here: http://www.gpower.hhu.de/en.html
Like, Comment, and Subscribe for more content like this

Views: 8039
Design eLearning Tutorials

An example of calculating power and the probability of a Type II error (beta), in the context of a two-tailed Z test for one mean. Much of the underlying logic holds for other types of tests as well.
I have a related video with a one-tailed Z test example available at http://youtu.be/BJZpx7Mdde4.

Views: 140825
jbstatistics

This video illustrates how to calculate power for a Pearson correlation coefficient. We look at the sample size required to get a desired power level (.80 is generally recommended) for for different values of Pearson r.
G Power

Views: 12237
Quantitative Specialists

This video tutorial shows you how to calculate the power of a one-sample and two-sample tests on means. The code will soon be on my blog page. Here is the link to the page with the syntax. http://threestandarddeviationsaway.blogspot.com/p/calculating-power-in-r.html

Views: 18144
Ed Boone

Using G*Power to Determine Sample Size

Views: 43317
Dr. Ubirathan Miranda

Views: 27726
Arthur Bangert

Examples for conducting a priori and post hoc power analyses in G*Power for paired-samples and independent-samples t-tests.

Views: 45774
miamipsych293

This video will introduce how to calculate statistical power in R using the pwr package.
All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/

Views: 8200
Center for Open Science

SKIP AHEAD:
0:39 – Null Hypothesis Definition
1:42 – Alternative Hypothesis Definition
3:12 – Type 1 Error (Type I Error)
4:16 – Type 2 Error (Type II Error)
4:43 – Power and beta
6:33 – p-Value
8:39 – Alpha and statistical significance
14:15 – Statistical hypothesis testing (t-test, ANOVA & Chi Squared)
For the text of this video click here http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/
For my video on Confidence Intervals click here http://www.stomponstep1.com/confidence-interval-interpretation-95-confidence-interval-90-99/

Views: 487238
Stomp On Step 1

A video on how to calculate the sample size. Includes discussion on how the standard deviation impacts sample size too.
Like us on: http://www.facebook.com/PartyMoreStudyLess
Related Video
How to calculate Samples Size Proportions
http://youtu.be/LGFqxJdk20o

Views: 312054
statisticsfun

A central concern in social science research is statistical power, or the ability of a given analysis to reliably detect the presence or absence of any effect(s). Without enough participants, an effect may in fact exist, but the researcher may be unable to detect it and falsely conclude that it does not exist. Conversely, with too many participants, clinically insignificant effects may reach statistical significance. Using examples, this presentation focuses on how to use G*Power software to determine how many participants are needed to reliably detect—or safely reject—the existence of effects in the real world. Attendees should download G*Power at this site before joining the meeting: http://www.gpower.hhu.de/en.html
Chicago School students can download the presentation slides here: https://tcsedsystem-my.sharepoint.com/personal/kglazek_thechicagoschool_edu/_layouts/15/guestaccess.aspx?guestaccesstoken=q6HTQO94Nfd%2bON2JM1Wdbpa76j8f2XtTMrVuHNgZdXQ%3d&docid=2_1c127379ce4ed4998a93aea43d440e737&rev=1

Views: 6888
Methodology Related Presentations - TCSPP

Power and Error Calculations in Hypothesis Testing and Statistics with Examples: What is Power(sensitivity) in Statistics and How to Calculate it? What factors influence errors in hypothesis testing and power of the test? How can we increase power of a test in research and statistics?
👉🏼 Errors and Power in Statistics Video: ( https://youtu.be/OYbc3uKpGmg ); Sensitivity, Specificity, Positive and Negative Predictive Values Video (https://youtu.be/eeM7KPRNlSs)
👍🏼Best Statistics & R Programming Language Tutorials: ( https://goo.gl/4vDQzT )
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In this statistics video lecture we learn about statistical power of a hypothesis test and type II error in statistics and in research.This tutorial covers the concept of power in statistics, how statistical power can be calculated, and the factors that affect power.
Here, we explore more in detail how the Power is related to alpha, the sample size (n), and the difference we wish to detect. The goal is to use this as a foundation for understanding the concept of power, and the factors that affect it. While power calculations can become quite complicated very quickly, the underlying concept is always the same. This video should lay a foundation for understanding power as a concept.
Some of these terminology can be confusing at first. when "rejecting a null hypothesis", this is referred to as a "positive test result". "failing to reject the null" is a "negative test result" (much like disease testing, null is that you don’t have disease, alternative is that you do have the disease, and testing positive means we reject the null and conclude that you have the disease, and vice versa). A Type I error is when we reject the null when in reality it is true. when we reject the null this is a "positive test result" and if in reality this is incorrect, it is a "false positive".
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Views: 2053
MarinStatsLectures- R Programming & Statistics

Dale W. Usner, Ph.D., President at SDC, explains the basics of statistical power for non-statisticians, highlighting what you need to know about statistical power, how it affects your clinical trial, and what to ask for from your statistician.

Views: 953
Statistics & Data Corporation

I address the issue of what sample size you need to conduct a multiple regression analysis.

Views: 17862
how2stats

This tutorial demonstrates how to calculate statistical power using SPSS.

Views: 117982
Amanda Rockinson-Szapkiw

Illustrating the use of the Excel simulation, and PiFace, to do power analysis, as discussed in Tutorial 1. Also includes comments on software, sample size and effect size.

Views: 4570
Keith McGuinness

Shows how to conduct a statistical power analysis, as well as determine minimum sample size requirements, in a structural equation modeling (SEM) analysis using the software WarpPLS.

Views: 1007
scriptwarp

Views: 6913
Elizabeth Lynch

Statistical Power is a conditional probability of a correct decision given that there truly is an effect. Power is affected by the sample size, the effect size, the alpha, and the variance.

Views: 3944
Frank Rust

Learn what a power analysis is and how to run one using G*Power
Download G*Power: http://www.gpower.hhu.de/en.html
For questions: [email protected]

Views: 1711
The Psychology Series

This video tutorial shows how to calculate the sample size for tests on means using the R statistical software. The R code used in the video is available on the following blog page. http://threestandarddeviationsaway.blogspot.com/p/calculating-sample-size-in-r-means.html

Views: 24589
Ed Boone

In this video we will discus the concept of statistical power and how it relates to Type I and Type II errors.
NOTE: These videos were originally part of a special series of lectures derived from the material in crp241. There are references to other modules and to a program called statcrunch that was used for this series; don't worry about either. The videos stand on their own and cover topics relevant to the discussion and activities in crp241.

Views: 4541
Steve Grambow

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