The American Statistical Association’s (ASA) Ethical Guidelines for Statistical Practice (1999) warns that selecting one “significant” result from multiple analyses of the same data set poses a risk of incorrect conclusions and that failing to disclose the limits of conclusions drawn is highly misleading. Type the full ethical scenario you have selected. Respond to the scenario with no less than 100 words and cite the ethical standard (name and number – for example “1.07 Improper Complaints”
Scenario:
A researcher conducts a study to investigate the effectiveness of a new drug in reducing symptoms for a specific medical condition. The study involves multiple analyses of the same data set, including various subgroup analyses, outcome measures, and statistical tests. After conducting all the analyses, the researcher finds one statistically significant result that suggests a positive effect of the drug on a specific subgroup of participants. The researcher decides to report only this “significant” result in the published paper, omitting the other analyses and findings.
Response:
The ASA’s Ethical Standard 1.06, Selecting “Significant” Results, applies to this scenario. This standard cautions against selectively reporting only the “significant” findings from multiple analyses of the same data set and emphasizes the importance of disclosing all analyses conducted. By choosing to report only the one statistically significant result in the published paper, the researcher violates this ethical standard.
By selectively reporting a single significant result and omitting other analyses and findings, the researcher is at risk of drawing incorrect conclusions. This is because the decision to report only the significant result creates a biased representation of the entire study. It fails to provide a comprehensive understanding of the data and the validity of the overall conclusions.
Failing to disclose the limits of the conclusions drawn from the analysis is also highly misleading, as the complete picture of the study’s findings is not being presented. When researchers selectively report significant results, it can create a false perception of the effectiveness of the drug and potentially lead to inappropriate decisions being made in clinical practice.
To ensure scientific integrity, it is crucial for researchers to disclose all analyses conducted, regardless of their statistical significance. By doing so, other researchers and readers can assess the robustness of the findings and draw a more accurate interpretation of the study’s results. This transparency also helps prevent the potential misinterpretation or misrepresentation of the data, thereby maintaining the credibility of the research field as a whole.
In this scenario, the researcher’s decision to report only the significant result may stem from a desire to highlight a potentially noteworthy finding or obtain a favorable outcome for the study. However, it contradicts ethical principles that prioritize scientific rigor and transparency. It is important for researchers to remember that statistical significance alone is not sufficient to determine the validity and generalizability of the findings.
To address this ethical concern, the researcher should provide a comprehensive account of all the analyses conducted, including both significant and non-significant results. This allows readers to assess the strength and limitations of the findings based on the complete dataset. If space limitations prevent including all analyses in the main paper, supplementary materials or appendices can be used to provide a more comprehensive report.
In conclusion, selectively reporting only significant results from multiple analyses of the same data set violates the ASA’s Ethical Standard 1.06. It poses a risk of incorrect conclusions and is highly misleading. Researchers should prioritize transparency and present all analyses conducted, regardless of their statistical significance, to ensure the integrity and credibility of scientific research. By doing so, they foster a more accurate understanding of the data and contribute to the advancement of knowledge in their field.
