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: 13882
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
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Views: 38153
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: 95875
NurseKillam

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

Views: 55527
Keith Bower

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. Copyright 2011-2019 StataCorp LLC. All rights reserved.

Views: 62550
StataCorp LLC

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

Views: 7752
Matthew Novak

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

Views: 260650
jbstatistics

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: 16427
Andrew Jahn

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

Views: 48612
Terry Shaneyfelt

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: 5479
Quantitative Specialists

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

Views: 15048
Kunchok Dorjee

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: 38974
ProfessorParris

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: 111
Biostatistics By Design

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: 9533
The Doctoral Journey

Views: 20603
Elizabeth Lynch

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: 22852
Dr. Todd Grande

This lecture discusses utilizing power analysis in experimental design.

Views: 4638
Matthew Novak

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: 24249
Social Science Club

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: 308448
jbstatistics

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: 5109
Society for Personality and Social Psychology

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: 2078
Kathleen Sweetser

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: 8286
Design eLearning Tutorials

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: 4577
Keith McGuinness

Find the sample size for an experiment testing a mean using power analysis in Minitab 17.

Views: 3819
Erich Goldstein

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: 83853
StoneyP94

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: 4545
Steve Grambow

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: 141463
jbstatistics

This tutorial demonstrates how to calculate statistical power using SPSS.

Views: 118345
Amanda Rockinson-Szapkiw

Using G*Power to Determine Sample Size

Views: 43705
Dr. Ubirathan Miranda

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: 6953
Methodology Related Presentations - TCSPP

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

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: 13893
ProfessorParris

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: 8291
Center for Open Science

The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.
In this video I have discussed a very simple way to determine the Sample size with reference.

Views: 73207
My Easy Statistics

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: 18258
Ed Boone

Tutorial on how to calculate the Cohen d or effect size in for groups with different means. This test is used to compare two means.
http://www.Youtube.Com/statisticsfun
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 119477
statisticsfun

Views: 9870
Elizabeth Lynch

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: 492423
Stomp On Step 1

An explanation of statistical power and its use in statistics

Views: 724
Prof. Patrick Meyer

Views: 6933
Elizabeth Lynch

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: 955
Statistics & Data Corporation

Views: 349
Frances Chumney

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: 2729
Center for Open Science

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

Views: 18057
how2stats

Views: 206
Christine Rabinak

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: 1726
The Psychology Series

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: 1024
scriptwarp

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