u/jadexiaohui

Hi everyone, I am asking this question as I am currently confused about the usage of p-value correction tests in hypothesis testing, such as FDR and Bonferroni correction tests, especially in research papers. My apologies in advance if it seems to be an unconventional question, it just seems like no one has questioned it before.

Based on my understanding, these tests should be used when there are multiple hypothesis tests carried out simultaneously. So to say, if one has a matrix plot of features - for example: height, width and weight of 2 populations, and pairwise comparison tests are used to test for significant differences in each metric across the 2 populations, a p-value correction would usually be used in a research paper to reduce the possibility of Type 1 errors.

However, what if the aforementioned matrix plot was separated into different charts in a sections of a research paper? Does a p-value correction still need to be used here? If yes, by this logic, wouldn’t that mean that p-value correction would have to be performed for all statistical tests of the same type in the entire paper? Wouldn’t performing a p-value correction for so many comparisons pose a risk of over-correction as well?

Thank you in advance for the advice, and please feel free to correct me if I was wrong in my understanding.

Hi everyone, my question is above in the title… Based on what I read - generalised logistic regression does not have any distributional assumptions that are required to be met but I just wanted to confirm as I’ve seen people saying otherwise as well.

Thank you in advance for the help!

Hi all,

I’m a beginner to biostatistics and I’m currently measuring protein expression in two independent cell lines. For each cell line, there are 3 independent biological replicates. There are 20 technical replicates (aka. repeated measurements) for each biological replicate.

My understanding is that the technical replicates are not independent observations, so treating all 60 measurements per cell line as independent samples would lead to pseudoreplication. Therefore, I am planning to average the 20 technical replicates within each biological replicate, leaving me with 3 observations per cell line.

I have two questions:

1. Is it statistically appropriate to perform an independent-samples t-test (or Welch's t-test) on the biological replicate means when there are only n = 3 biological replicates per group? Some sources seem to suggest this is acceptable, while others discourage the use of statistical tests for n = 3. Hence, I am unsure whether this method is valid, especially in the context of biostatistics.

2. For visualisation, would it be misleading to plot all technical replicates in a violin plot while overlaying only the 3 biological replicate means as points? My concern is that the violin shape would be driven largely by technical variation (60 observations per cell line), whereas the statistical inference is based on only 3 biological replicates per cell line. I have also considered using superplots, but in my case they become visually cluttered due to the large number of technical replicates.

Thank you very much in advance for the advice.

Hi everyone, I have activity data from treatment and control cohorts measured in biological samples. Each sample is recorded across multiple timepoints (different days), and each box in my boxplot pools all measurements across days within each cohort.

From my understanding, measurements from the same sample across different timepoints are not independent, since they come from repeated measurements of the same sample.

Is it still valid to use a Mann–Whitney U test to compare treatment vs control cohorts in this case, even though the independence assumption is violated? If not, what would be the correct statistical approach for this dataset?

I have heard that mixed-effects models are appropriate, but I would prefer a simpler pairwise test if possible (e.g., something that could still support significance annotations on boxplots - such as significant bars for p-values)

Thank you!