This scenario raises ethical concerns around selective analysis. While Abi has not manipulated the raw data, choosing to highlight only results that favour the cereal’s nutritional value could still be misleading. Presenting analyses that downplay or exclude harmful findings may constitute statistical bias by omission, thereby undermining the research’s integrity (Panneerselvam, 2014).
The ACM (2018) Code of Ethics (sections 1.2 and 1.3) emphasises honesty and the duty to avoid harm. Abi has a responsibility to report all credible findings, both positive and negative, in a balanced and transparent manner. Choosing to focus only on results that support the manufacturer’s claim could be ethically comparable to outright distorting the data.
There are also legal implications. Regulations like the UK Consumer Protection from Unfair Trading Regulations prohibit the use of misleading information in product marketing (GOV.UK, 2008). If Abi’s report contributes to a public health risk, the consequences could extend beyond professional misconduct.
Abi’s role as a statistical programmer does not absolve him of accountability for how his work is used. If he believes the manufacturer may ignore negative findings, he could submit a full report with clear documentation or involve an independent ethics committee to review the outcome. Upholding transparency is vital to maintaining public trust in scientific research.
References
Association for Computing Machinery (2018) ACM Code of Ethics and Professional Conduct. Available at: https://www.acm.org/code-of-ethics.
GOV.UK (2008) GUIDANCE on the UK Regulations (May 2008) implementing the Unfair Commercial Practices Directive. Available at: https://assets.publishing.service.gov.uk/media/5a74d389e5274a3cb28677f4/oft1008.pdf.
Panneerselvam, R. (2014) Research Methodology. 2nd edn. Delhi, India: PHI Learning.