Three Mistakes You Make When Applying Biostatistical Methods
19th December 2022 By MARS Research Hub

Three Mistakes You Make When Applying Biostatistical Methods

Mistake #1 - Neglect calculating representative sample size in your clinical research

The sample size is one of the most important considerations in a clinical trial. It determines the power of the study and can have a significant impact on the results. In the case of small sample size, it is likely that the study will not be able to detect meaningful differences between groups or provide sufficient evidence for conclusions about safety or efficacy.

In some cases, such as when researchers are evaluating an intervention that they suspect will be effective, they might want to use an underpowered study design by choosing a small sample size in order to demonstrate effectiveness quickly. However, if researchers use an underpowered design and it turns out that their hypothesis was incorrect, then they may have wasted resources and time on an ineffective intervention.

Mistake #2 - Thinking that correlation implies causation

It's important to know the difference between correlation and causation. If a person smokes a pack of cigarettes every day and gets lung cancer, it doesn't mean that smoking caused the cancer. For example, there could be other factors not accounted for in the study that caused both of those events to happen.

Correlation is used to establish a link between two or more sets of data. This can be in the form of finding a trend or pattern between two groups of data. Causation, however, is used to establish the cause-effect relationship.

Mistake #3 - Ignoring potential confounding effect in modeling clinical variables

The confounding effect of a variable is the effect of the variable on the relationship between an exposure and an outcome, which may be either positive or negative. A confounder is a factor that may influence both the exposure and the outcome.

Ignoring potential confounding effects in modeling clinical variables can lead to incorrect conclusions about treatment effects.

In order to avoid this, researchers need to identify all potential confounders and use statistical methods that account for these confounding effects.


Biostatistics is a very powerful tool that can be used to make informed decisions. However, it can also be misunderstood and misused. Biostatistics is not a crystal ball that reveals the future, but it can give you an idea of what might happen under certain circumstances.

Biostatistics are not always 100% accurate, but they are often close to the truth. This means that there will always be some margin of error when using statistics. The most important thing to remember when using biostatistics is to take into account the margin of error and use them only if they are relevant to your situation.

Mistakes happen when people misuse biostatistics or don't understand them properly. It's important to know how statistics work before you start using them in your research or decision-making process so that you avoid these common mistakes and get better conclusion from your research.