8 Things Everyone is Doing Wrong in Statistical Analysis

We often hear that the result of an old research study contradicts the new studies. It is because there are many statistical mistakes that anyone can make. Most of these mistakes occur because of the lack of understanding of various statistical techniques, their limitation and proper use. You can only become an expert in the statistical analysis if you know some common mistakes that can be made while conducting your analysis for the research.

What Is Statistical Analysis?

It is a discipline that deals with collecting data and uncovering its trends and patterns. Simply put, it is just another way to say “statistics”. In this, after the collection of data, you organize it for analysis. After this, you summarize the data for interpretation and presentation.

A simple example of statistical analysis is that you collect data on grades scored by students in a class, and then you make a pie chart from this data for analysis to see whats the grade average of the class.

Mistakes That Everyone Makes In Statistical Analysis:

One of the most common questions researchers ask during their research is, “is my statistical analysis right?” well, this article can help you answer that question. Here are the eight common mistakes that everyone in statistical analysis makes.

Ignoring Control Groups:

Control groups in statistical analysis are the statistical portion of participants, which are isolated from other variables. Sometimes ignoring the appropriate control group or condition can lead to incorrect results in your analysis. For example, teachers’ interventions don’t need to be the leading cause of college students’ poor grades. There can be other factors involved.

Failing To Cope With Uncertainty:

Scientists usually refer to statistics as a science of “uncertainty”. We cannot have 100% certainty in our statistical analysis. Uncertainty is everywhere. It is prevalent for researchers to fail in dealing with uncertainty. For this, it is essential to understand that suitable statistical methods and techniques cannot eradicate uncertainty, but they could help you to get some understanding of it. With the help of these methods, you can see patterns to quantify uncertainty. Expecting certainty only from a single study is also not correct. You can only get a high level of certainty in your analysis through several pieces of evidence and high-quality research. However, in case of any difficulty, you can get best PhD dissertation help.

Using Small Samples:

Using small samples for research is another common statistical mistake. The large size of samples decreases the size of the impact that can be very significant in your research. With small sample sizes, you will need more participants for better observation of the effect. Therefore, the smaller the sample size, the greater the chances of getting false results. Because of the significant effect size, you must avoid this mistake of thinking that a small sample size can give you accurate results. For example, if you observe an effect of a problem with a small data of only ten participants, you may reach a false conclusion, and the size of your sample is an issue.

Use Of Incorrect Analysis Method:

Different data analysis techniques have different model assumptions. Your technique will give you the correct results only if the model assumptions are relevant to the context of your research data. Therefore, it is vital to ask the following questions while using a statistical analysis method:

  • What will be the model assumptions for this statistical method?
  • Are these model assumptions relevant to the situation or condition being studied?

It is a widespread statistical mistake that researchers neglect these two questions. Researchers sometimes only check some of the model assumptions and miss other essential assumptions in the process.


Double-dipping is also called circular analysis. It means using the exact data more than one time. It is another common mistake in statistical analysis. You must test your hypotheses on a different sample from the one that generated your hypothesis. Similarly, evaluating simulation models against trials different from the one you used to determine parameters is also important. Without this, you are less likely to face double-dipping.

Doing Over-Interpretation:

Many researchers often over-interpret the outcomes and results of statistical analysis. There are many common mistakes of over-interpretation, such as:

  • Many times, we run an experiment on a particular group. However, due to over-interpretation, we find results for general people.
  • Similarly, we also sometimes extrapolate beyond the range of our data.
  • Researchers sometimes ignore ecological validity by extrapolating people with features that differ from those relevant to the study.
  • It also leads to only considering the statistical significance and ignoring practical relevance.

The Flexibility Of Statistical Analysis:

You might have heard inflation bias, p-hacking or selective reporting. It means misreporting the accurate sizes of effect in your analysis. Researchers usually use some flexibility in analysis to search for possible vital effects. It is better to prevent this in your analysis content to avoid getting false positives.

Errors In Sampling:

Many researchers make statistical errors when they do not select the correct sample for representing the whole population chosen for the study. This leads to the generation of incorrect results that are different from those needed based on the entire population. The occurrence of sampling error is widespread in statistical analysis. Therefore, it is crucial to have some error margins while researching.

Final Thoughts:

From data collection to analysis, statistical mistakes can occur during any research phase. We have highlighted some mistakes that almost everyone makes while conducting statistical analysis. From this guide, you now know that you must need to avoid sampling error, double-dipping, p-hacking, and over-interpretation while doing your research. Keeping these errors in mind, you can significantly get more correct results relevant to your research.

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