Statistical blunders that data scientists may encounter

Statistical blunders that data scientists may encounter

Statistics is very important in the field of data science. To dig up data, clean it, and store it, you need to use data visualization techniques and apply statistical rules. So, beginners or even experts encounter some mistakes while managing data. In this article, we are going to highlight common statistical blunders to solve them. Here are the following errors.

Pie Charts :

Have you ever used it in the pie charts? To exhibit categorical values, pie charts are of importance. There might be some mistakes while making them. Here are the following errors.

  • Percentage can be added up to 100%.
  • You can’t fit 3D in the pie charts because it is not possible for it.

Biased Data :

In statistical terminology, when we underestimate or overestimate a value, it is called bias data. Common errors may happen in such data. Here are multiple reasons for it.

  • Faults in the instruments measuring values and increasing or decreasing them. 
  • Being on one side like during a survey, taking data from a particular group instead of the whole community. For example, you get information only from the sportsman while leaving others during the survey of the effects of sports patterns. 
  • Asking for inappropriate or invaluable questions during the survey can cause biased data.

Nonrandom sample:

Once you have understood biased data, it is effortless to determine this one as well. Nonrandom samples are also based on particular factors making it biased data. As we know, biased data can cause difficulty and produce errors, same is the case with nonrandom sampling. Try to analyze data based on random samples, otherwise, you might encounter multiple issues.

Botched Numbers :

Errors are quite possible while applying statistical rules due to designs or some other mistakes. So, what we can do in this concern. You need to make sure all your data analysis has not botched numbers in the output. 

  • Every statistics technique you applied is the best match for the data.
  • Determine response rate by dividing the number of responses by the number of total people you asked for.
  • Demonstrate values and find out errors in typing the values.


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