Statistical analysis plays a crucial role in biomedical research by helping us understand complicated data and reach meaningful conclusions. But it is important to interpret statistical results correctly, or else we might end up going in the wrong direction, jeopardizing the integrity of our findings. The dangers of misinterpreting statistical data are significant. It can lead to false conclusions, wasted resources, and even misguided medical decisions. As biomedical research involves people’s lives and well-being, accurately understanding statistical findings becomes incredibly important. To help you navigate the statistical landscape confidently, here are some key aspects you need to consider when interpreting your results:
Effect Size:
Pay attention to the effect size, which indicates the magnitude of the relationship or difference between the variables you are studying. Statistical significance alone doesn’t tell you how large or clinically meaningful the effect is. Effect size helps put the results into perspective.
Example: Let’s say we have a study where they’re trying to figure out which weight loss method works better between Intervention A and Intervention B. The P values they calculate might indicate that Intervention A leads to significantly more weight loss than Intervention B. However, the effect size, which measures the actual difference in weight loss between the two groups, will tell us how important or meaningful that difference is in a real-world sense. It helps us understand whether Intervention A is truly more helpful than Intervention B in terms of weight loss.
Type of Analysis:
The type of statistical analysis you conduct will affect the conclusions you draw from your data. A common error is making causal inferences on the basis of correlation/association data. In other words, you infer that X causes Y when your data only show that X is correlated or associated with Y at a single point in time. Let’s look at an example:
Example: In a cross-sectional community-based study, you find a correlation between hours spent in daycare and incidence of dental caries in 3–5-year-olds. However, this doesn’t mean that being in daycare causes dental caries. Instead, your finding warrants further exploration: did you control for family socioeconomic status? Did you stratify daycares by the kind of food/snacks provided to the children? Does this finding hold even for daycares where children bring in their own food?
Data Quality:
The quality of your data can directly influence your results. High-quality data, obtained through rigorous experimental design, accurate measurements, and reliable data collection methods, increases the reliability and validity of the research findings. It allows you to make more confident and accurate inferences about the phenomenon under investigation. Conversely, missing or erroneous data can introduce substantial uncertainties and biases into the analysis, leading to flawed or misleading conclusions.
Example: While conducting a study examining patients’ response to a new antihypertensive agent, you realize you have several missing observations. To address missing data, you use the Last Observation Carried Forward (LOCF) method, replacing a participant’s missing values after dropout with the last available value for that participant. LOCF has been widely criticized because it assumes that the participant’s responses (e.g., blood pressure) would have been stable from the point of dropout to trial completion, rather than increasing or decreasing further. LOCF also assumes that missing values are “missing completely at random” (i.e., that the probability of a participant dropping out of your study is not related to drug side effects, disease severity, etc.). Hence, using LOCF could potentially bias your results and skew your conclusions.
An experienced biostatistician can help you safeguard your research from incorrect conclusions or inaccurate inferences. Check out Editage’s Statistical Analysis & Review Services today!
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