Which statement best defines p-hacking?

Which statement best defines p-hacking?

• A. A statistical method for correcting p-values.
• B. A strategy to maximize p-values.
• C. Research practices that lead to selective reporting of significant p-values.
• D. A method to estimate p-values by simulation.

Answer: (C)

P-hacking involves using flexibility in data collection and analysis to obtain a statistically significant result and then reporting that finding. It happens when researchers try many different analyses, measures, models, or data exclusions, or decide to stop collecting data early, and only publish or highlight the significant p-values. The problem is that this selective reporting inflates the chance of finding “significant” results by luck rather than by a true effect, undermining trust in the evidence. This is not a statistical method for correcting p-values, nor a technique to increase p-values, nor a way to estimate p-values by simulation. Rather, it’s about the practice of presenting only the favorable, significant outcomes that spring from flexible analytic choices.

P-hacking involves using flexibility in data collection and analysis to obtain a statistically significant result and then reporting that finding. It happens when researchers try many different analyses, measures, models, or data exclusions, or decide to stop collecting data early, and only publish or highlight the significant p-values. The problem is that this selective reporting inflates the chance of finding “significant” results by luck rather than by a true effect, undermining trust in the evidence.

This is not a statistical method for correcting p-values, nor a technique to increase p-values, nor a way to estimate p-values by simulation. Rather, it’s about the practice of presenting only the favorable, significant outcomes that spring from flexible analytic choices.