Avoiding Pitfalls in Data Analysis

With even a small mistake in the analysis, you can hamper the results of your research and the entire process can go waste. Therefore, it is important to carefully go through the data analysis stage and ensure you do it the right way. Following are some of the common pitfalls that must be avoided in order to succeed in your analysis:

Confirmation Bias

When you have a hypothesis in front of you, you tend to look for data patterns that will support your hypothesis, ignoring the data that might highly reject it. To avoid this, never approach data analysis with a specific outcome in mind. You can also assign someone to be your devil’s advocate who will give an opposing perspective to your thought process.

Irrelevant data and distraction

You will surely come across some data that is not relevant to your study. Stay away from such distractions as it may cost you a lot of time and energy without any desired results. Frame the boundaries for your data analysis and ensure you stay within them.

Causation and Correlation

Many students tend to mix the cause of a situation with correlation. If an action causes another, it is definitely correlated. However, if two things occurred at the same time, they might not be correlated even though it may seem so. The best way to combat this is by proving the null. Try to eliminate the variable that you think might be causing the particular action.

Statistical Significance

If you use small data sets or compare results that are not different from each other, you will not get the desired statistical significance. Get the right data size at the beginning itself to avoid this problem.

By being aware of the possible pitfalls in data analysis, you can surely combat them strongly and emerge successful with your data analysis methods.