A continuación, te explicamos cómo puedes navegar por los conflictos que surgen de las diferentes interpretaciones de los resultados de la investigación.

One thing I’ve found helpful is acknowledging my own personal bias. We often operate within the boundaries of what we have experienced. Whenever there are varying perspectives, I often remind myself that I will be biased towards what I already know without realizing it; it helps to be open to other perspectives.

In addition to the above statement embracing a collaborative approach in research can help mitigate individual biases. By engaging in open discussions, peer reviews, and interdisciplinary teamwork, researchers can challenge their assumptions and enhance the robustness of their conclusions. This collective effort not only enriches the research process but also fosters a culture of continuous learning and improvement.

Almost all research, when first published, is interpreted not just by what has previously been published, but also in the light of what the authors know, but which is not yet published. This is not bias, but additional “privileged” insight.

We as experimentalists always come up with our own interpretation of the data. Knowingly unknowingly we include bias in the interpretation. It is very important to describe in detail in the notebook or meetings why and how you analyse the data as it should be. This will ensure that the experiments can be repeated without considerable error by yourself and also by someone who is doing the same after you.

Example: During the development of a new vaccine, team members may have differing interpretations of the clinical trial results. To address this, facilitate a roundtable discussion where epidemiologists, immunologists, and clinical researchers can openly share their perspectives on the data. By actively listening to each expert's reasoning and also pen down the ideas, the team can gain a comprehensive understanding of the vaccine's efficacy and safety, potentially identifying critical factors that might have been missed in initial analyses.

Additionally, incorporating diverse methodological perspectives can strengthen the study. By integrating qualitative and quantitative approaches, or considering alternative analytical techniques, researchers can gain a more comprehensive understanding of the data. This methodological pluralism helps to cross-verify results and reduce the likelihood of methodological biases, leading to more robust and credible research outcomes.

And, with modern publishing trends to minimise methodology, increasingly difficult to interpret. Only with precise detailed information about methodology can data be truly dissected.

Example: In a pharmaceutical study assessing a new drug's effectiveness, conflicting interpretations may stem from different statistical approaches used to analyze the results. Bring the statisticians and researchers together to review the methodologies employed. This could involve re-examining the study design, the statistical models applied, and the experimental controls used. By ensuring that the methods are trustworthy and correctly implemented, the team can achieve consensus on the interpretation of the drug's performance.

All literature, not just that that has been published recently. And through reference to original articles, not reviews. Anyone who has read Asimov’s Foundation will get this!

Example: While working on a novel treatment for a chronic illness, team members may have varying interpretations of patient response data. To resolve this, consult recent journal articles and analyses that address similar treatments. Comparing your findings with established research can help clarify how your data fits within the broader scientific context. This literature review can provide evidence that supports or not the differing interpretations, guiding the team towards a more informed and accurate conclusion. This is important because you will be doing your experiments based on the study.

It doesn’t hurt to give trusted peers a sight of a draft manuscript - you will be far more likely to get constructive input this way than from peer review.

The best way to handle conflicts over different interpretations of research findings is to focus on the actual data. Everyone should back up their views with specific evidence. It's also really helpful to keep detailed records of all the different interpretations and the reasons behind them. This way, we can always look back and see how we reached our conclusions. Plus, having clear criteria for what makes an interpretation valid, like how reproducible or statistically significant it is, helps us agree on the most solid conclusions.