I have 9 datasets with one predictor and one target attribute. For each of the dataset, I am testing for a single hypothesis - whether the attributes are associated. My question is - I have got the following:

• Uncorrected p-values: 8 out of 9 p-values are significant ($p\le\alpha$)
• Bonferroni correction (FWER): 3 out of 9 p-values are significant ($p\le\alpha_{corrected}$)
• Benjamini–Hochberg correction (FDR): 6 out of 9 p-values are significant ($p\le\alpha_{B\&H}$)

I could combine 9 datasets but I am testing for each dataset separately because the context of the data in each dataset is important.

Question: Based on these findings, should I accept or reject the null hypothesis (the 2 attributes are not correlated?) and what could be the formal reasoning behind that?

The model is expected to produce few FP/FN but we are not sure to which extent. So we can allow a few errors from the model.

I have 9 datasets with one predictor and one target attribute. For each of the dataset, I am testing for a single hypothesis - whether the attributes are associated. I have got the following based on the test-statistic:

• Uncorrected p-values: 8 out of 9 p-values are significant ($p\le\alpha$)
• Bonferroni correction (FWER): 3 out of 9 p-values are significant ($p\le\alpha_{corrected}$)
• Benjamini–Hochberg correction (FDR): 6 out of 9 p-values are significant ($p\le\alpha_{B\&H}$)

I could combine 9 datasets but I am testing for each dataset separately because the context of the data in each dataset is important.

Question: Based on these findings, should I accept or reject the null hypothesis (the 2 attributes are not correlated?) and what could be the formal reasoning behind that?

The model is expected to produce few FP/FN but we are not sure to which extent. So we can allow a few errors from the model.