P-Hacking: What It Is and Why It's Unethical

P-hacking is unethical because it does not follow a clear motive or hypothesis. It is difficult for the audience of the reported research to determine the strength in relationships within the research samples, analytical method(s) used, or the judgment calls that the researcher makes. P-hacking is error-prone, and manipulation of results to artificially get significant p-values by the researchers is possible. P-hacking signifies problems in the planning and transparency of researchers. The media and society should not be quick to publish, promote, or accept reported researches that have not been scientifically tested. The media and society should demand a straightforward scientific process and hypothesis, coupled with adherence to certified scientific procedures. The reported research should be verifiable and replicable and conducting trials of the research in less risky environments before full-scale adoption or approval is necessary. Not everything published is accurate. Society and media should demand an analysis plan and adherence levels of the research question about its hypothesis. They should further investigate statistical parameters used and reports of any changes, data collection period, and scope and make several comparisons and replications of the research before applying or promoting the same. P-hacking could affect the amounts of chemicals allowed in food and water or the laws that politicians or legislators pass. P-hacking affects all aspects of day-to-day lives, and cases such as P-hacked research that instructs people to stop vaccinating against, say, polio, measles, or hepatitis could lead to long-term health issues or even death. The problem is the application of reported research transcends all spheres of human life, and the adoption of any false research outcomes could cause death or health problems. I will be more diligent and careful when consuming research or conducting research than before using research, not to fall victim to using op-hacked values. I will have a clearly defined hypothesis and use the Bonferroni correction to reduce the significant p-value depending on statistical test outcome changes. I will perfect planning for the critical analyses and always document them for easy remembrance when conducting the analyses. I will avoid many unplanned tests or concealing their outcomes in the reporting of the research.