When it comes to statistic analysis, there are two classifications: descriptive statistics and inferential statistics. In a nutshell, descriptive statistics intend to describe a big hunk of data with summary charts and tables, but do not attempt to draw conclusions about the population from which the sample was taken. You are simply summarizing the data you have with pretty charts and graphs–kind of like telling someone the key points of a book (executive summary) as opposed to just handing them a thick book (raw data).
Conversely, with inferential statistics, you are testing a hypothesis and drawing conclusions about a population, based on your sample. In this case, you are going to run into fancy sounding concepts like ANOVA, T-Test, Chi-Squared, confidence interval, regression, etc., but we’ll save those for another day.
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To understand the simple difference between descriptive and inferential statistics, all you need to remember is that descriptive statistics summarize your current dataset and inferential statistics aim to draw conclusions about an additional population outside of your dataset.
Perhaps these concepts are most easily explained with some examples…
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