Data gaps in quantitative data analysis - what are they and how to deal with them?
Missing data in the context of data analysis refers to situations where there are no values for certain variables or observations in a dataset. In other words, they are places where a number, text, or some other form of data was expected, but for various reasons was not there. Missing data can take…
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Population pyramid
When looking for the best way to visualise the data you have, you will come across an impressively wide range of different types of charts - from simple, basic ones such as a scatter plot to very advanced ones such as a Sankey diagram. Some, however, are designed with a specific type of data in min…
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The three sigma rule
The three sigma rule is an important tool in statistics and quality management. In the context of data analysis, it allows the identification of outlier points that are significantly different from the rest of the data. The use of the three-sigma rule in quality control also allows anomalies to be …
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Segmentation: from grouping to classification
Segmentation is a key process in data analysis, dividing a data set into relatively homogeneous groups based on specific criteria. The purpose of segmentation is to identify hidden patterns, differences and similarities between objects in a dataset, enabling more precise and relevant analyses. Two …
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Recoding quantitative variables into qualitative ones – techniques and their practical application
When analysing the data, we take into account both quantitative information (such as salary, age, number of products ordered) and qualitative information (e.g. gender, education, level of satisfaction with service). In order to make it easier to work with the data or to adapt it to a specific stati…
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Outlier or anomaly? Detection of abnormal observations
Can one abnormal occurrence cause concern? Based on one deviation from the norm, should a red light start flashing? Of course! In many industries and businesses, an anomaly is a sign that must be reacted to quickly and efficiently in order to prevent consequences. So how do you recognise an anomaly…
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Statistical inference
Statistical inference is the branch of statistics through which it becomes possible to describe, analyse and make inferences about the whole population on the basis of a sample.
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Outlier cases. Identification and significance in data analysis
In data analysis, it is important to identify unusual observations that are significantly different from the others. Such values, called outliers or outlier cases, can affect the results of statistical analysis and lead to erroneous conclusions. In this material we will look at what outliers are, t…
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Levels of measurement
The level of measurement is one of the most important properties of variables. It determines which statistical tests will be available to the researcher during the course of the analysis. But what information does it convey to us specifically? A level of measurement is a pattern of measurement that…
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