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  • Writer's pictureAlec Fufidio

Why One Data Point Is Not Enough

An unfortunate truth often seen is the attempt to support a claim or a validation using only a single data point. It is a common issue in our industry, from analytical methods to cleaning validation.

It is impossible to definitively state any trend or pattern with a single data point, even if that data conforms to your expectations. For example, let’s say we are validating the dirty hold time with one run. The run is successful. However, what can we really state with this single trial? Can we say that our cleaning process is effective? The answer is “No.” We don’t have enough data to say that there were no external factors, such as the drying conditions, manual manipulations, that will impact the cleaning process. Can we say that our cleaning process is robust or repeatable? Again, the answer is “No.” We only know that the cleaning process worked in this single trial. With only a single data point, we cannot determine if this run represents a pattern or is an outlier.

Let’s look at an example in industry where a lab made bold claims with insufficient data. This lab was verifying its microbial enumeration technique and utilized a single plate to claim that its technique was suitable and robust. There were no variations in test conditions and no replicates. While it is possible that this method was suitable, the data presented did not support that claim. Just as with our hypothetical cleaning scenario, it is impossible to support any claim of robustness, when robustness requires multiple experiments, each with deliberate variations in test conditions. This leaves this lab vulnerable to making decisions based off bad data, and being totally unaware, as any systemic issues with the method would go unnoticed.

Replicates are crucial in all scientific experiments, contributing significantly to confidence in research findings. They enhance the robustness and accuracy of the results, allowing us to account for variability and outliers and increasing confidence in our conclusions. Replicates help ensure that the observed effects are not merely due to chance and are reflective of the specific test conditions. Replicates build a characterization of the studied trend that attempt to reflect reality as closely as possible.

Replicates are required to establish the reproducibility of experimental results. Studies should produce consistent outcomes when repeated under similar conditions. Replicates enable us to assess the consistency of our findings across multiple iterations, bolstering the certainty of the results.

In addition, replicates help identify and control for potential confounding factors that may influence the outcome of an experiment. In complex systems, multiple variables can interact and impact the results in unanticipated ways. Replicating experiments allows us to account for these potential confounders and distinguish between the effects of the manipulated variables and other external factors. Replicates enhance the study’s internal validity, ensuring that any observed effects can be confidently attributed to the manipulated conditions.

In the broader context of scientific industry, replicates foster a culture of transparency and accountability. Sharing detailed information about experimental procedures and outcomes, including replicates, allows health agencies to critically evaluate the work and draw their own conclusions. If a robust dataset is used, all parties involved can draw similar conclusions, as no wishful thinking is required. This transparency also facilitates refining and advancing processes and institutional knowledge over time. In essence, replicates in experiments are indispensable for promoting the integrity and robustness of scientific goals.

If a health agency comes knocking at your door, don’t find yourself defending the indefensible. When planning experiments, ensure that a proper number of replicates follow suit.

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