If you are doing explorative analysis 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 expressed the statistical significance of an experiment to measure an effect.

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.

If you are doing explorative analysis 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 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 expressed the statistical significance of an experiment to measure an effect.

If you are doing explorative analysis 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 expressed the statistical significance of an experiment to measure an effect.