Is this p-hacking? I'm currently looking into the gender pay gap using data from glass door (found via kaggle). The dataset has columns for gender, age, performance evaluations of employees, seniority, pay etc.
For context: I have learned a lot of Data Science/Machine learning/programming over the past few years, and am just doing a few of my own basic portfolio projects for practice, before applying for jobs.
I have done a fairly naive t-test, comparing average pay for men vs average pay for women. I am now looking to add in controls, comparing similar age groups, seniority, education level etc. I want to do more t-tests, as well as looking at a chi-square distribution and/or ANOVA.
As I do multiple tests A/B tests, I want to avoid p-hacking. I have a few hypotheses, for example I expect the pay gap to be greater for older age groups. But this is mostly exploring the data, I don't have a single hypothesis I am looking to prove for the entire study, nor do I have a political agenda.
I'm not sure it would really count as p-hacking as long as I choose which comparisons to make, and report everything. I would think it's only p-hacking if I selected which t-test results to report to help prove a hypothesis. Is this fair?
And another question (forget my data for a moment), with ANOVA, as it compares multiple groups at once to look for significance, is this not p-hacking?

If you are doing explorative analysis, then you don't care about p-values. What you do is search for any pattern. P-values are used to verify a hypothesis, but you have none.
However, if after your explorative analysis you are gonna perform some hypothesis tests with the same data then this gives the erroneous p-values if the hypothesis were created by the same data.
If you only have a single data set available then you can split the data into two subsets, one for analysis and another for follow-up research to verify whether the found patterns are much different from statistical variations in the sampling.

You seem to be doing a search for patterns by using hypothesis tests and p-values. That is not p-hacking if you regard the p-values only as an aid in pattern recognition (a search for anomalies) instead of a value to report in relation to an experiment to verify a certain effect.
You have to be careful though that you do not switch the meaning from a statistic used in pattern recognition to a value that expresses the statistical significance of an experiment to measure an effect.
