types of statistical tools for data analysis

There isn’t a precise mathematical definition of outliers. Other errors can be caused by miscalculations, data contamination, human error, and more. For example, the limitations of measurement instruments or procedures can mean that the correct data is simply not obtainable. Change in the behavior of the observed systemĭata collection errors are a particularly prominent cause of outliers.There are many possible causes of outliers, but here are a few to start you off: OutliersĪn outlier is a data point that differs significantly from the majority of the data taken from a sample or population. That way, you’ll be able to use the sample to glean conclusions about the population. Ideally, the sample should preserve the essential statistical features of the population to a satisfactory extent. This subset of a population is called a sample. That’s why statisticians usually try to make some conclusions about a population by choosing and examining a representative subset of that population. Populations are often vast, which makes them inappropriate for collecting and analyzing data. In statistics, the population is a set of all elements or items that you’re interested in. You’ll learn how to understand and calculate these measures with Python. Useful measures include covariance and the correlation coefficient. Correlation or joint variability tells you about the relation between a pair of variables in a dataset.Useful measures include variance and standard deviation.

Variability tells you about the spread of the data.Useful measures include the mean, median, and mode. Central tendency tells you about the centers of the data.In this tutorial, you’ll learn about the following types of measures in descriptive statistics:

Types of statistical tools for data analysis