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[Q] Wilcoxon-Mann-Whitney test doubt

submitted by vixxovs to biostatistics [link] [comments]

[Q] Wilcoxon-Mann-Whitney test doubt

When Wilcoxon-Mann-Whitney (non-parametric sum ranks test) is explained in textbooks for small samples is told that:
- m is the numerousness of the smallest sample
- n is the numerousness of the biggest sample
and m + n = N
For m or n > 10 we can approximate the sum of the ranks Wx to a normal distribution with mean:
$ \mu=\frac{m*(N+1)}{2} $
Can you confirm me that m is
the sample with the smallest numerousness
NOT the sample with lowest sum of ranks (Wx) ?
submitted by vixxovs to statistics [link] [comments]

Is Wilcoxon-Mann-Whitney the appropriate test for my data?

Hi all!
So, I had this experiment where I would present several successive visual cues to my readers and I would ask them to rate each cue from 1 to 10 (no decimal values). The rating distribution ended up being non-normal because most ratings for each cue would be the same (e.g., mostly 1 or 2 for the first cue, mostly 3 for the second with very few exceptions...). I would then collect the average ratings for every cue and compare them with each other pairwise (e.g., 1st cue compared to 3rd cue etc.) to see whether there were any significant differences in the averages.
After discussing it with my supervisor, I then decided to use Wilcoxon-Mann-Whitney for my test, as my data had one independent variable (the cues) and one dependent variable (the effect of the cues) and I was comparing the averages of two non-normal distributions, therefore a non-parametric alternative to the t-test seemed best suited.
However, the final reviewer said he couldn't understand why I had used WMW, as I only had one dependent variable which I was treating as an interval (?). So I'm a bit lost now. Isn't WMW also appropriate when you have one dependent variable? Sorry if the question sounds dumb, I am not a statistician and before this experiment I knew nothing about this.
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[Q] Non-parametric test choice: Mann-Whitney U-test or Wilcoxon sign test?

Hello, I am currently writing my thesis and am comparing IPO short-term returns. I am conducting a paired two sample t-test for means on the returns, variance, skewness and kurtosis (data sample = 1800 IPOs). My supervisor instructed me to also conduct the same test using a non-parametric test. I have no idea which test would suit my data sample best, I'm assuming either Mann-Whitney or Wilcoxon. Any suggestions/guidance?
- STATS Novice
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CI [0 - 0] and p value < .05 in Wilcoxon-Mann-Whitney test, how is that possible? How to interpret it?

I performed an Wilcoxon-Mann-Whitney test in R, and this is what I got from one of the variables:
Mean score group 1 0.05 Mean Score group 2 (control) 0.45 W 251 Lower CI: 0 Higher CI: 0 p-value: 0.04
I mean... if the p-value is 4... it means the effect is most likely (96%) not due to chance, right? So how the hell do I have an CI that is not crossing zero, but exactly zero? Does this mean I'm 96% sure there is a effect on the population due to the variable I'm researching on (difference in scores In a cogitive task between musicians and non-musicians), and not due to chance (h0 supposedly rejected), but that this significant effect is like... None? But if there were no effect, how could this be "non-effect" be significant? What?
Is this most likely due to the difference being significant, but extremely small (I mean, a diff in means of 0.40 is literally less than one "question" in this 50 - "questions" test. So yeah, quite small. Btw, I used something like 6 significant digits for R, but if the CI was something like -0.000something on the lower end, I may have rounded that when trowing that into my article's word file. Maybe that's whats causing my puzzlement?
Btw thanks in advance, light bearing internet folk!!!
submitted by Skeptical_Hippie to AskStatistics [link] [comments]

[Statistics Part 06] Difference between groups of numeric data in non-parametric situation: Mann Whitney U test, Wilcoxon signed rank test, Kruskal Wallis H test

[Statistics Part 06] Difference between groups of numeric data in non-parametric situation: Mann Whitney U test, Wilcoxon signed rank test, Kruskal Wallis H test submitted by datalabbd to u/datalabbd [link] [comments]

Comparing two-groups using Mann-Whitney-Wilcoxon test in RStudio

R beginner here! I have the waiting time data for two groups (group 1 (n=17) = walk-ins, group 2 (n=30) = scheduled appointments). I'm trying to analyze via RStudio if there's a difference in the waiting time if you have a walk-in versus a scheduled appointment. I believe the MWC test is appropriate to use since my data isn't normally distributed. Is this the right way to go about doing this? Thanks!
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[Question] Comparing 2 groups: Mann-Whitney-U or Wilcoxon test for my dataset?

I have a list of comments from YouTube.
usernameChannelId | hasTopic | sentiment | commentId a | 1 | 4 | xyxe24 a | 0 | 2 | h5hssd a | 1 | 3 | k785hg a | 0 | 2 | j7kgbf b | 1 | -2 | 76hjf2 c | 0 | -1 | 3gqash c | 1 | 2 | ptkfja c | 0 | -2 | gbe5gs c | 1 | 1 | hghggd 
I want to test if the average sentiment for comments for topic videos is statistically different than comments for nontopic videos.
Each comment is assigned to a YouTube account. Each comment has also a variable TRUE/FALSE (1, 0) if the comment is for a video with a specific topic or not + the sentiment of the topic.
A user can have multiple comments (guess that means related/matched samples?, more than 1 value for the same user) or only 1 comment (so unrelated/unmatched samples in the same dataset??).
But I'm unsure what test to take?
submitted by Yojihito to statistics [link] [comments]

[Q] Mann Whitney Wilcoxon test vs Kruskal Wallis vs T Test and interpertation

I am confused on which is appropriate to use . I was testing a categorical variable with 3 groups against some continuous variables. Using graphs and the shapiro wilk the continuous variables are not normal. To test this I used the Kruskal Wallis test. Other similar research papers on this topic used this test as well. (this is for work)

Now i am testing a categorical variables with 2 groups (yes/no) against the same continuous variables. The samples i have are greater than 30. Some of the things i read say that as long as the sample size is large enough the t-test would still be okay. But this being non-normal data i am leaning toward the Mann Whitney U test. (Maybe incorrectly)
A. Which test is better to use and how do you interpret the results of this test.

To put some context. I am comparing the use of a certain suturing procedure and one of the continuous variables is Procedure time.
So the median time is 226 when the suturing procedure is NOT used compared to 165 when it is used.
Or the mean is 225 vs 170.
If i used the t test i could say something like, the average procedure time is significantly shorter in the yes group compared to the no group. But i am not clear if i could say the median procedure time is significantly shorter if i use the Mann Whitney U test - How do you turn the results of this test into plain English ?
submitted by Vervain7 to statistics [link] [comments]

Mann Whitney U or Wilcoxon Sign test?

I'm having a hard time picking between the two tests. I have a pre/post survey but not everyone completed the post survey. I wanted to include everyone into the data analysis and was wondering if it would be appropriate to conduct a Mann Whitney U test instead of decreasing my population size by doing the Wilcoxon Sign Test.
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Can we we apply tests like Wilcoxon-Mann-Whitney on a small dataset?

I’m trying to find if a new teaching style has an impact on women grades in a CS class( comparing 5 women from previous class and 9 in the new class). The data is not normally distributed. or is it too small to get a reliable results?
submitted by NouraAlba to AskStatistics [link] [comments]

Non-parametric, one sample, single tailed independent t-test alternative. Mann Whitney? Wilcoxon matched- pairs? ...other?

I have 45 measurements from a population, and I want to test if the mean value is significantly different from a value of zero. The data has many 0 values, so the assumption of normality is violated. I'm unsure whether wilcoxon matched pairs or a mann whitney is appropriate.
Bonus points for an example in python!
The test value of zero allows us to test the hypothesis that the presence of non-zero values within that condition are due to noise noise.
Thanks, guys!
Update: - The data represent counts and therefore contain no negative values.
submitted by eyeballjunk to AskStatistics [link] [comments]

Wilcoxon Signed rank test or the Mann Whitney U test for Matched Control Group.

Hey Guys! Hope y’all are well. I want to compare two groups after an intervention - An Experimental Group and a Control group which was matched based on Gender, Age and Scores on the Pre Test. I’m aware that we use Mann Whitney for two different samples and Wilcoxon for related samples. I’m not sure which of these conditions my case comes under. Can y’all tell me what test will be appropriate?
submitted by Gowtham_Raajasekar to psychologystudents [link] [comments]

[Question] Confidence levels of Wilcoxon and Whitney-Mann tests

I ran a few Wilcoxon and Whitney-Mann tests on R on some paired and unpaired non-normal samples. The samples have different sizes (one smaller, around 30-35, and one >60) and each one is divided into two equal-sized groups, whose results I'm confronting through the test. My advisor told me that it still makes sense to run the test on the smaller samples, but the confidence levels will be lower. I'm not sure what that means. I still get p values <0,05 sometimes even in that he just completely wrong? (sorry for the possibly dumb question, I'm a total newbie in this)
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Question about sample size calculation for Wilcoxon-Mann-Whitney-U test?

Hey everyone,
I'm having a bit of trouble with calculating a sample size for a small project that I'm doing. I want to know use a pain scale (0 = no pain, 10 = worst possible pain imaginable) between 2 different anesthetics.
The minimum difference we are looking for is 2 points between the two groups.
Based on a SD of 2.4 from another study, I used a formula to find the sample size for a t-test (n = f(α/2, β) × 2 × σ2 / (μ1 − μ2)2) assuming alpha at 5% and power of 80%. Then, using the worst-case scenario recommended here ( and dividing the sample size by 0.864, I get a sample size of about 54 (27 in each group).
My questions are:
1) Is this methodology "correct" for finding the sample size necessary? 2) Is there another way to calculate the sample size directly for a MWU-test? I am not too sure but I feel that the methodology I used is a bit too roundabout.
Thanks! (Sorry, still a bit of a statistics newb).
submitted by FarBehind404 to AskStatistics [link] [comments]

Hello guys, I need help on how to make a T test comparison of means and a U Mann Whitney test in SPSS, I have no idea how it works. Ty fellas :D

Hello guys, I need help on how to make a T test comparison of means and a U Mann Whitney test in SPSS, I have no idea how it works. Ty fellas :D submitted by SandiaJugadora to spss [link] [comments]

Mann-Whitney Wilcoxon Heterocedasticity

First of all, I apologise for any grammatical errors, as English is not my mother tongue. I am working with some data with 11 cases; with a quantitative variable and a factor (treatment). My data in R are as follows:
Q <- c(0.52257929, 0.59409435, 0.59990197, 0.43122963, 0.54950869, 0.53396515, 0.59540344, 0.07396450, 0.55989774, 0.04496018, 0.35368660) Treatment <- c("C", "A", "C", "A", "C", "A", "C", "A", "A", "A", "A" ) df <- data.frame(Q, Treatment) 

I am trying to apply the Mann-Whitney -Wilcoxon test (based on ranks) to test whether the data from both treatments belong to the same population, given that the sample size is small. One of the assumptions of this test is homoscedasticity; so I have applied the Fligner test, which I understand is more robust to non-normal data. In these data I find heteroscedasticity, and after trying the log and square root transformations, I have found that the difference in variance remains. I don't know what the next step is. Any advice is welcome. Thanks in advance!
submitted by pcuatrocinco to AskStatistics [link] [comments]

Mann-Whitney vs Wilcoxon signed rank

I know that Mann-Whitney tests independent samples and the Wilcox sign test tests dependent samples, but I'm having trouble matching this to something concrete. Does anyone have any examples of when we would use one test vs the other.
My specific use case is a ab test of visits to a website. I'm thinking I would do Mann-Whitney, but I'm not sure.
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[Question] Can I use Mann-Whitney U test to compare a variable when I have two unequal groups (n= 4 and 6)?

submitted by SuccessfulAd9033 to statistics [link] [comments]

12 in the Kruskal Wallis and Mann-Whitney/ Wilcoxon equations

Hi! I’m wondering where the (n(n+1))/12 and (n1n2 (n1+n2+1)/12 come from in both the Kruskal Wallis and Mann Whitney equations respectively. I understand that it represents the variance but I don’t quite understand why or how the above formulas are used instead of (n2 -1)/12? When you figure out the variance of n ranks using a variance formula the answer is (n2 -1)/12 so I’m unsure as to why that is not the formula used in the test statistic equations. How were the other variance equations derived? I would assume they are all related in some way as they are all over 12
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[Q] Advice on Mann-Whitney U-Test vs. Kolmogorov-Smirnov-Test for Time Samples

Hi, I'm evaluating whether to use the Mann-Whitney U-Test (MWU) or the Kolmogorov-Smirnov-Test (KS) for comparing time samples (e.g. from performance benchmarks).
It seems KS outperforms MWU on some very obviously different distributions: (Red and green are the different distributions, black is overlap. For the following, p < 0.05 for KS and p >> 0.05 for MWU. This is not real data.)
My thinking is that because MWU tests whether two randomly selected samples have the same probability of being larger and smaller, it can not properly "detect" these cases because the change is mostly symmetrical. Is that roughly correct?
MWU on the other hand also rejects the null hypothesis in some cases where KS does not, although the reason is not obvious to me:
In practice, KS and MWU seem to perform similarly. I'd prefer MWU for the intuitive effect size measure, but it kind of hard to argue in favor of it with the obviously different distributions (first images) not rejected...
Any advice or pointers to good resources are appreciated. Thanks!
submitted by kthxb to statistics [link] [comments]

GraphPad Mann-Whitney U test

GraphPad Mann-Whitney U test
Hey, I have run the Mann-Whitney U test on GraphPad Prism version 9.4.1 for Windows, but some analysis has no p-value as shown in the figure. Is it because the mean ranks are too similar to each other? Or it means something goes wrong with my data?
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İstatistik Ders5 (Bağımlı Non-Parametrik Testler) Correlation Analysis - concept & Coefficient of ... Statistics 101: Linear Regression, The Very Basics - YouTube Wilcoxon Signed Rank Test in R with Example  R Tutorial 4 ...

About Quick-R. R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve.I created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R. Se aplicó la prueba t-student y U de Mann Whitney en el análisis bivariado y para determinar el patrón de consumo de alimentos y se efectuó un análisis de clases latentes (ACL).Resultados: los niños tienen un consumo de energía superior a su requerimiento, pero existe diferencia significativa según el estado nutricional; el consumo en los niños obesos es de 1.632 ... Aeejay [email protected] Blogger 200 1 25, ... 84 Contrastes de comparación de dos poblaciones <841 a Contraste de la mediana Contraste de Wilcoxon-Mann-Whitney . 8421 Equivalencia del estadístico W de Wilcoxon y . el estadístico de Mann-Whitney 843 Contraste de SiegeLTukey . wwwFreeLibroscom. 85 Contraste de comparación de más de dos poblaciones 630. 851 Contraste de Kruskal-Wallis 631. 852 Comparaciones múltiples 640. 91 ... X-ray photoelectron spectroscopy on 1-peso and 2-pesos of the Argentine Republic. NASA Astrophysics Data System (ADS) Gard, Faramarz S.; Duffo, Gustavo; Bergamasco, Pablo; Forlere

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İstatistik Ders5 (Bağımlı Non-Parametrik Testler)

Stata Tutorial: Combining Graphs - Duration: 9 ... Use of Wilcoxon and Mann-Whitney-U tests (introduction to Non-parametric tests) - Duration: 11:15. Cengiz ÖLMEZ 2,305 views. 11:15 . Anova ... Description Wilcoxon Signed Rank Test in R with Example: Learn how to conduct Wilcoxon Signed Rank test (non-parametric alternative to paired t-test) in R. 👉🏼Related: Wil... Bienvenue sur la chaîne YouTube de Boursorama ! Le portail compte plus de 30 millions de visites mensuelles et plus de 290 millions de pages v... This is the first Statistics 101 video in what will be, or is (depending on when you are watching this) a multi part video series about Simple Linear Regression...