Statistical treatment of data – What, Why & How??

Statistical treatment of data is an imperative part of studying any field. It is an effective and essential way out for using the data in the right form. Collecting the raw data is just a tiny step of any experiment or analysis. But a study with no conclusions, experiments mean nothing. And that’s what a statistical treatment does for researchers.

What does statistical treatment mean?

Statistics is a science of learning and understanding data. Having statistical knowledge helps use the right methods to collect the data, employ an accurate analysis, and present significant results from it. All-in-all statistics is a crucial procedure in making decisions based on the data and make predictions.

statistical treatment presents the raw data in the analytical form using statistical tools like mean, mode, median, regression, conditional probability, sampling, standard deviation, and distribution range. And using these tools is not easy for a layman. It needs adequate statistics know-how.

Statistical analysis of data consists of the variable(s). And the data analysis could be univariate or multivariate, depending upon the number of variables involved. If the data is implying in multiple variables, then the multivariate analysis should be performed. Similarly, if the data is singular in number, then the univariate analysis is preferred.

Statistics: – Descriptive and inferential statistics

There are two ways of expressing data in the statistics, first, descriptive, and second inferential statistics.

In descriptive statistics, one tries to describe the relationship between variables in a population. It provides a summary of the data using a central tendency.

The central tendency can define the extent to which the observations come together around a central position. There are three measures of central tendency – mean, median, and mode.

On the other hand, inferential statistics take out the random sample from a population to draw inferences about the whole population. It serves a significant role when it’s impossible to assess the data of each member of the population. Inferential tools make use of hypotheses to craft the conclusions. Thus, here the purpose is to answer or test a specific hypothesis.

A hypothesis is an imaginary phenomenon that may be considered for future decision making or implementing. Parametric and non-parametric tests can be conducted to implement this type of statistical treatment. 

How to Avoid Common snags!?

Implementing statistical analysis to produce adequate interpretations is a lengthy process. It starts from constructing the study frame, choosing and evaluating the variables, formulating the apt sampling technique and sample size, organizing the data, and deciding the right analysis methodology. The entire quality and reliability of the results depend on this series of events. And a single or tiny pitfall might create misleading results.

Hence, one should know these below-mentioned tricks to detect and cut out the pitfalls in between the analysis.

Biased decisions- A significant reason for false interpretations is bias decisions. Giving prejudice and taking intuitions into a statistical analysis is not a sane choice. It can mislead the conclusion right from the beginning. Choose to be practical while statistical treatment.

Overgeneralization- This term means that you’re generalizing one population’s findings to every other population without proper assessment. This is not correct, as it’s not clear what features are differentiating one population from another. And statistical conclusions can be valid but always have some limitations too, and one must understand those limitations.

Causality – While doing statistical analysis, one needs an entire set of standards to assume causality relations. As for how would you determine when X causes a change in Y? Many assume it as a casual relationship. But you should know and use the experimental models to confidently assume the causality.

Using incorrect analytical tools- There are many different sets of statistical tools for every problem and study. And one can use a wide range of tools, but not all of them will be correct for a specific condition. One needs to find out the best approach for their data. Like when we use multivariate analysis where the data has more than one variable. But, if used with one variable data, it may not give true or any results. So, select the viable tool that the data is demanding.

Violating assumptions- Most statistical analysis follows assumptions for the best results. And these assumptions are often related to properties of the samples, variables, data, and models. The difficult part is that one can give-up some assumptions for specific data. But when you violate an important assumption, the analysis is at risk of crafting misleading results.

Data mining-

It is possible that if the data is investigated for too-long, the results can significantly become false. When too many tests are conducted, some will be good enough just because of the chance pattern in the data. But picking up a particular number of performing tests during a study can place the results in the proper framework.

Why is statistical treatment necessary?

Statistical data is essential because of these significant reasons:

  • Producing reliable data
  • Analyzing the data appropriately
  • Drawing reasonable conclusions

Statistical treatment serves a learning purpose to data and helps in finding out common issues that can mislead the results. Also, it assists in drawing critical assessment of the data presented, consequently helps in making fruitful decisions.

For instance, someone suggests that 4 out of 5 dentists prefer a specific toothpaste. Then it is nothing but a result of keen statistical analysis. It makes the data more reliable and understandable. It helps to learn and to put up a reasonable discussion.

Statistics allow you to evaluate the claims based on quantitative evidence and helps in differentiating the right and a dubious conclusion. And this is a must thing, especially for today’s world as the data is plentiful and so the interpretations presented by many people with their understanding or unknown motivations.

But a true and correct statistical analysis could help an individual produce a trustworthy analysis and make viable predictions. Besides, this also guides in not falling into different analytical traps. There are different sets of tools predefined for every type of data analysis.

And, when they are used aptly, it provides accurate results. Statistical data can help in avoiding any unforeseen uncertainties and in locating errors.


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