Statistical Treatment of Data – Explained & Example

Community Blog

Keep up-to-date on postgraduate related issues with our quick reads written by students, postdocs, professors and industry leaders.

Statistical Treatment of Data – Explained & Example

Statistical Treatment of Data in Research


‘Statistical treatment’ is when you apply a statistical method to a data set to draw meaning from it. Statistical treatment can be either descriptive statistics, which describes the relationship between variables in a population, or inferential statistics, which tests a hypothesis by making inferences from the collected data.

Introduction to Statistical Treatment in Research

Every research student, regardless of whether they are a biologist, computer scientist or psychologist, must have a basic understanding of statistical treatment if their study is to be reliable.

This is because designing experiments and collecting data are only a small part of conducting research. The other components, which are often not so well understood by new researchers, are the analysis, interpretation and presentation of the data. This is just as important, if not more important, as this is where meaning is extracted from the study.

What is Statistical Treatment of Data?

Statistical treatment of data is when you apply some form of statistical method to a data set to transform it from a group of meaningless numbers into meaningful output.

Statistical treatment of data involves the use of statistical methods such as:

  • mean,
  • mode,
  • median,
  • regression,
  • conditional probability,
  • sampling,
  • standard deviation and
  • distribution range.

These statistical methods allow us to investigate the statistical relationships between the data and identify possible errors in the study.

In addition to being able to identify trends, statistical treatment also allows us to organise and process our data in the first place. This is because when carrying out statistical analysis of our data, it is generally more useful to draw several conclusions for each subgroup within our population than to draw a single, more general conclusion for the whole population. However, to do this, we need to be able to classify the population into different subgroups so that we can later break down our data in the same way before analysing it.

Statistical Treatment Example – Quantitative Research

Statistical Treatment of Data Example

For a statistical treatment of data example, consider a medical study that is investigating the effect of a drug on the human population. As the drug can affect different people in different ways based on parameters such as gender, age and race, the researchers would want to group the data into different subgroups based on these parameters to determine how each one affects the effectiveness of the drug. Categorising the data in this way is an example of performing basic statistical treatment.

Type of Errors

A fundamental part of statistical treatment is using statistical methods to identify possible outliers and errors. No matter how careful we are, all experiments are subject to inaccuracies resulting from two types of errors: systematic errors and random errors.

Systematic errors are errors associated with either the equipment being used to collect the data or with the method in which they are used. Random errors are errors that occur unknowingly or unpredictably in the experimental configuration, such as internal deformations within specimens or small voltage fluctuations in measurement testing instruments.

These experimental errors, in turn, can lead to two types of conclusion errors: type I errors and type II errors. A type I error is a false positive which occurs when a researcher rejects a true null hypothesis. On the other hand, a type II error is a false negative which occurs when a researcher fails to reject a false null hypothesis.

Share on facebook
Share on twitter
Share on linkedin
Share on pinterest
What is Tenure Track?
What Is Tenure Track?

Tenure is a permanent position awarded to professors showing excellence in research and teaching. Find out more about the competitive position!

Read More »



Join thousands of other students and stay up to date with the latest PhD programmes, funding opportunities and advice.

Browse PhDs Now

Other Posts
New PhD Student
5 Tips for A New PhD Student

Starting your PhD can feel like a daunting, exciting and special time. They’ll be so much to think about – here are a few tips to help you get started.

Read More »
Danny Ward Profile
Danny Ward

Danny is a third year PhD student at the John Innes Centre and the University of East Anglia, working with Pseudomonas bacteria to understand how they infect their hosts.

Read More »
Julia Ravey

Julia’s in her final year of her PhD at University College London. Her research is helping to better understand how Alzheimer’s disease arises, which could lead to new successful therapeutics.

Read More »
Dr Ipsa Jain

Dr Jain gained her PhD in Molecular Oncology from the Indian Institute of Science. She is now a science illustrator and communicator, and works with to initiate conversations around sci-art and women in science.

Read More »

Browse PhDs Now

Join Thousands of Students

Join thousands of other students and stay up to date with the latest PhD programmes, funding opportunities and advice.