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Nowadays confidence intervals (CIs) are requested in most journals of experimental psychology. It would be nice to see them in ezStats. The usual CI is 95%, but, if you decide so, you may add a new option so users can select their preferred level.
I created my own function to easily calculate CIs, but I do not know how to incorporate it into you code. I left it here in case that it helps.
sem = SD/sqrt(N) # Compute the Standard Error of the Mean; SD = standard deviation; N = N
error = qt(0.975,df=N-1)*sem
ci.hi = med + error # Upper limit of the CI
ci.lo = med - error # Lower limit of the CI
The text was updated successfully, but these errors were encountered:
The CI for a mean is rarely a practical value. Particularly in cases when a variable is manipulated within-Ss, where such CIs sum both variance of the effect and variance of the Ss' intercepts. ezStats gives the N and SD for those that really want to compute these CIs by hand. Note that ezStats also gives the FLSD, which is more useful as it derives its value from the associated ANOVA thereby removing sources of variance that aren't (typically) of interest (like Ss' intercepts). Note also that the CI for an effect is FLSD/sqrt(2). Your suggestion for adding an argument to change the CI %, where it is currently hard-coded at 95%, is probably reasonable.
Thanks for your answer and your time. Despite your arguments, CIs over the means are still usually requested, but you are right in that it is easy to compute them from the output of ezStats. I will do it for my own.
Nowadays confidence intervals (CIs) are requested in most journals of experimental psychology. It would be nice to see them in ezStats. The usual CI is 95%, but, if you decide so, you may add a new option so users can select their preferred level.
I created my own function to easily calculate CIs, but I do not know how to incorporate it into you code. I left it here in case that it helps.
sem = SD/sqrt(N) # Compute the Standard Error of the Mean; SD = standard deviation; N = N
error = qt(0.975,df=N-1)*sem
ci.hi = med + error # Upper limit of the CI
ci.lo = med - error # Lower limit of the CI
The text was updated successfully, but these errors were encountered: