Continuing on with my Data Analytics learnings, with a jump into Descriptive Statistics vs Inferential Statistics and an attempt to clear up Population vs Sample from my last blog post.
Descriptive Statistics
Descriptive statistics takes a sample, e.g. a group and records data about that sample. The data is then presented in summary statistics and graphs to present properties of the group. There is no uncertainty about the properties as they are describing only the group (e.g. people or items) measured. Descriptive statistics are not used to infer properties about a larger population.
Inferential Statistics
Takes data from a sample, and then makes inferences about the larger population from which the sample was drawn. The goal of Inferential Statistics is to draw conclusions from a sample and generalise those conclusions to a population. To do this there must be confidence that the sample used accurately reflects the population.
Population
A population is all the entities that exist in the domain of the investigation. This can be difficult to document (e.g., documenting every single human being on planet Earth) but can be more manageable if smaller populations are used (e.g., all employees of a organisation, all pupils registered on a course).
Sample
A sample is a subset of the population used for statistical investigation because populations can be too large / too complex to obtain. For example could if looking at the employees of an organisation may choose a sample of 10% to record details about.
Measures of Dispersion
The Measures of Dispersion include the standard deviation and variance (discussed in this blog post), the interquartile range and the range. The range can be shown using the equation:
r = Max {x} – Min {x}
So for the following numbers: 3, 5, 7, 9, 11, the range (r) would be:
r = 11 – 3
r = 8
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