Rules of thumb on magnitudes of effect sizes
Rules of thumb on magnitudes of effect sizes
15 Matching Annotations
- Dec 2020
-
imaging.mrc-cbu.cam.ac.uk imaging.mrc-cbu.cam.ac.uk
-
- Nov 2020
-
statistics.laerd.com statistics.laerd.com
-
Assumption #4:
When to choose between the Kruskall-Wallis mean ranks and the median test.
-
-
statistics.laerd.com statistics.laerd.com
-
You will want to report this as follows:
Reporting ANOVA in APA style.
-
- Oct 2020
-
www.scribbr.com www.scribbr.com
-
Example: Reporting the results of ANOVA
Example: Reporting the results of ANOVA
Tags
Annotators
URL
-
- Sep 2020
-
moderndive.com moderndive.com
-
9.2 Understanding hypothesis tests
Hypothesis testing
-
- Mar 2020
-
utstat.toronto.edu utstat.toronto.edu
-
2.6 The Normal Distribution
The normal distribution in r
-
-
vincentarelbundock.github.io vincentarelbundock.github.io
-
sleep Student's Sleep Data
Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.
-
Pulse Pulse Rates and Exercise
Students in a Stat2 class recorded resting pulse rates (in class), did three "laps" walking up/down a nearby set of stairs, and then measured their pulse rate after the exercise. They provided additional information about height, weight, exercise, and smoking habits via a survey.
-
HSAUR agefat Total Body Composision Data
Dataset used for KSB
-
-
stats.idre.ucla.edu stats.idre.ucla.edu
-
Factors that affect power
Factors that affect power.
-
Cohen’s recommendations: Jacob Cohen has many well-known publications regarding issues of power and power analyses, including some recommendations about effect sizes that you can use when doing your power analysis. Many researchers (including Cohen) consider the use of such recommendations as a last resort, when a thorough literature review has failed to reveal any useful numbers and a pilot study is either not possible or not feasible. From Cohen (1988, pages 24-27):
Recommendations from Cohen about choosing the effect size when doing a power analysis.
-
Obtaining the necessary numbers to do a power analysis
Obtaining the necessary numbers to do a power analysis
-
Power is the probability of detecting an effect, given that the effect is really there. In other words, it is the probability of rejecting the null hypothesis when it is in fact false. For example, let’s say that we have a simple study with drug A and a placebo group, and that the drug truly is effective; the power is the probability of finding a difference between the two groups. So, imagine that we had a power of .8 and that this simple study was conducted many times. Having power of .8 means that 80% of the time, we would get a statistically significant difference between the drug A and placebo groups. This also means that 20% of the times that we run this experiment, we will not obtain a statistically significant effect between the two groups, even though there really is an effect in reality.
Power analysis definition
-
-
www.statmethods.net www.statmethods.net
-
Descriptive Statistic
R provides a wide range of functions for obtaining summary statistics. One method of obtaining descriptive statistics is to use the sapply( ) function with a specified summary statistic.
Tags
Annotators
URL
-
-
my.ilstu.edu my.ilstu.edu
-
The standard error (the standard deviation of the distribution of sample means) has this formula:
Tex command for the standard deviation of the sample mean
-