Great video! If I have a large F statistic, small p-value (less than 0.01), a very large effect size, but a weak Bayesian Effect size (R2=0.09), would I interpret the model as statistically significant, but that the model might not be trust worthy? Thanks for the feedback
There always will be disagreements when you ask several tests, it's unfortunately normal. But I would go with the test you prefer and describe it in the M&M very exact. In this way there is no right or wrong, there is just a method and the answer the method produced. It's fair and comparable to other studies. Thanks for your feedback and for watching!
Olá, não, infelizmente não. Para isso, eu recomendaria usar um modelo normal (lm, glm etc.) e usar o pacote {emmeans}, que revela os melhores resultados de interações na minha opinião. Tenho um vídeo sobre o pacote {emmeans} em geral e um vídeo extra sobre interações com {emmeans}. Acho que é o que você precisa.
absolutely! convenience! normality uses shapiro.test in the background, but its tidy. So, that you can test normality of 1000s of samples in the one line of code: mtcars %>% group_by(cyl) %>% normality(mpg) can't do it with shapiro.test()
I have trully "bad" data. Small sample size in a crossover study design (n=8). I have t0, t1, t2 (t0=baseline). Spent over 72 hours looking everything, I can't manage to find a proper test...my data is mixed (some of it normally, some not normally distributed), I did wilcoxon, but I do not have significant friedman's f to actually do it... SAVE ME To edit, I'd prolly ban myself from the channel, hope I do not disturb you too much! Sorry!
:) the thing I wanna say - "There is no such thing as bad data", but I see lot's of crappy data in my day job :). Well, I can't say for sure without seeing the data, but Friedman's test seems as the right choice. I did not understand what your are saying about Friedmans Significance, but if you already did a Friedmans test, and it's not significant, then it's correct. So, you have your answer. When you did not do it, look at the Freidmans video here and my Blog on it (in the video description). If you wanna be sure, you can do mixed-effects-model, then you are safe! Hope that helps.
@@yuzaR-Data-Science I'm looking for more than 2 decimals on my p values in the graph. Is this possible? I only get 0.05 for example, which seems significant but I'd love to know whats the exact value. Thanks alot!
Very concise and well explained. I will be sure to keep this collection of functions and your explanations handy! Thanks very much!
Glad it was helpful! Thanks for watching again, Zane! Cheers
Love your work. Thank you for creating these.
Thanks a ton! I am glad it’s useful
Good explaination
Thanks again! I hope you like the rest of the videos too! Thanks for watching
Great video! If I have a large F statistic, small p-value (less than 0.01), a very large effect size, but a weak Bayesian Effect size (R2=0.09), would I interpret the model as statistically significant, but that the model might not be trust worthy? Thanks for the feedback
There always will be disagreements when you ask several tests, it's unfortunately normal. But I would go with the test you prefer and describe it in the M&M very exact. In this way there is no right or wrong, there is just a method and the answer the method produced. It's fair and comparable to other studies. Thanks for your feedback and for watching!
could you please upload video for ploting barplot for comparing two groups with p value and error bars?
Thanks for the idea. I personally would never use those. But I know a lot of people do, so, I’ll put it on the list.
Uma dúvida: como fazer ANOVA (Two-Way) usando o pacote ggbetweenstats. É possivel rodar o modelo interagindo variáveis? Muito grato!
Olá, não, infelizmente não. Para isso, eu recomendaria usar um modelo normal (lm, glm etc.) e usar o pacote {emmeans}, que revela os melhores resultados de interações na minha opinião. Tenho um vídeo sobre o pacote {emmeans} em geral e um vídeo extra sobre interações com {emmeans}. Acho que é o que você precisa.
@@yuzaR-Data-Science Muito grato pelas orientações! Obrigado!!!
Is there a reason to use dlookr::normality() instead of shapiro.test()?
absolutely! convenience! normality uses shapiro.test in the background, but its tidy. So, that you can test normality of 1000s of samples in the one line of code:
mtcars %>% group_by(cyl) %>% normality(mpg)
can't do it with shapiro.test()
@@yuzaR-Data-Science *FYI :-) :*
Funciton in S4-Class:
assumtionsTest=function(df,myFormula){
assumTest %
dplyr::mutate(varPval= as.numeric(car::leveneTest(myFormula,.)[1,3])) %>%
dplyr::group_by(sampleSize) %>%
dplyr::summarise(
varPval=varPval[1]
,varDiffConfirmed = confirmVarDiff(varPval)
,varDiffPvalType = confirmPvalType(varPval)
,normPval = shapiro.test(value)['p.value']
,normPvalType = confirmPvalType(normPval)
,normConfirmed = confirmNormality(normPval)) %>%
dplyr::ungroup()
return(assumTest)
#--------------------------------------------------
Trigger (3 lines :-)):
myFormula
cool! 😁👍
I have trully "bad" data. Small sample size in a crossover study design (n=8). I have t0, t1, t2 (t0=baseline). Spent over 72 hours looking everything, I can't manage to find a proper test...my data is mixed (some of it normally, some not normally distributed), I did wilcoxon, but I do not have significant friedman's f to actually do it... SAVE ME
To edit, I'd prolly ban myself from the channel, hope I do not disturb you too much! Sorry!
:) the thing I wanna say - "There is no such thing as bad data", but I see lot's of crappy data in my day job :). Well, I can't say for sure without seeing the data, but Friedman's test seems as the right choice. I did not understand what your are saying about Friedmans Significance, but if you already did a Friedmans test, and it's not significant, then it's correct. So, you have your answer. When you did not do it, look at the Freidmans video here and my Blog on it (in the video description). If you wanna be sure, you can do mixed-effects-model, then you are safe! Hope that helps.
@@yuzaR-Data-Science I'm looking for more than 2 decimals on my p values in the graph. Is this possible? I only get 0.05 for example, which seems significant but I'd love to know whats the exact value. Thanks alot!