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I would like to perform a power analysis on my pilot data. My test statistic is a single-sample mean with only 14 observations. … I understand that 'power analysis' is the wrong term for what I was looking for. I am really asking, "How much can I trust these 14 observations to tell me the mean?" …

I'm trying to do a power analysis for a research study aimed at characterizing between group differences in depression for 2 different populations. … For a P value <0.05, specified power of 0.8 and a difference of PHQ-9 scores between populations of 7 (scale is 0-27), what is the best/proper power analysis equation? …

At an interim analysis, $m < n$ subjects are recruited into the trial and the interim effect size is some value $\hat{\phi}_m < \phi$. Now we believe the power is not as high as anticipated. … We wish to perform a conditional power analysis; What is the probability of achieving a statistically significant result after recruiting all $n$ subjects? …

However, it seems that the normal approximation of the binomial distribution that is generally used for power analysis among comparisons of proportions is not appropriate here for at least two reasons: … p(success) is nowhere near 0.5 for the populations AND the expected number for Group B Failures, (2+59)*(2+28)/716 = 2.56, is below the guideline of 5 for all cells in chi-square power analysis. …

I need to perform a priori power analysis to determine the minimum sample size for the study we have designed. … I would greatly appreciate it if you could analyze the function implemented in R for calculating power analysis. …

So I'm working on my dissertation and I'm trying to figure out a binary logistic regression power analysis to show how many participants I should collect. … To run this power analysis in R, there were two packages I could use (powerMediation and WebPower). …

I would like to do power analyses for hypothesis tests of (non-)equality of proportions in which the proportions are very small. … I would like to do so without using normal (or Poisson) approximations of the binomial distribution. There are several general types of power questions I'd like to be able to address. …

I have been working on solving a problem that requires power analysis to be run in order to determine if the subset of data that we're running analysis on is sufficient before we do the analysis. … In other words I want to run power analysis and subsequent analysis (assuming power was high enough) for the first response variable + all independant variables and then run a separate analysis using the …

I want to perform a simplified power analysis to determine how many cells (samples) to have in control and experimental conditions (assume equal n across conditions). … For t test or normal distribution, the pwr package in R and determining cohen's d is simple, but I am not sure how to perform this when working with counts data following a poisson distribution. …

I'd like to do a power analysis for a single sample from binomial data, with $H_0: p = 0$, vs. $H_1: p = 0.001$, where $p$ is the proportion of successes in the population. … If $0 < p <1$, I could use either the normal approximation to binomial, or $\chi^2$-test, but with $p =0$, these both fail. I'd love to know if there is a way to do this analysis. …

How would you conduct power analysis for comparing means of a response that goes from 0 to 1? -- e.g. comparing the mean test scores of two groups of students, where the score can range 0-100%? … to assume that the 0-100% scores are close-enough-to normal. …

The most common design in my experiments is something like this (I use lme4): PhysioVar ~ Condition * Time + (1|Participant) + (1|Stimuli) Reviewers in journals always ask for an a priori power analysis … Resources I already know: I'm familiar with Westfall and Judd's online power analysis for MLM. …

For example, suppose I want to apply the permutation test to the following hypothesis that can be checked using a normal distribution. … For the power analysis, I want to find the power of the test if the true difference is K. How would I actually do the power analysis in this case? …

90% power at alpha = .05, the power analysis demonstrates at least 6 samples are needed in both groups model_weight1 <- lm(weight ~ treatment, data = data_weight) # Conventional NHST power simr::powerSim … in the treatment group, and then run the power analysis However, this basically makes the mean difference between the groups equal to 0, and, therefore, power will be extremely low no matter the sample …

I would like to calculate the power to detect a hypothetical x% decrease from a mean value, but have a few qualifying questions. … A log(x+1) was insufficient in transforming the data to resemble normal data. Is there a package in R that can run a power analysis on this data? …