Collecting, analyzing, and reporting with data can be daunting. The person that SAGE Publishing — the parent of MethodSpace — turns to when it has questions is Diana Aleman – editor extraordinaire for SAGE Stats and U.S. Political Stats. And now she is bringing her trials, tribulations, and expertise with data to you in this blog, Tips with Diana. Stay tuned for Diana’s experiences, tips, and tricks with finding, analyzing and visualizing data. View Diana’s blog in its native habitat HERE.
We talk a lot about statistics here and it occurred to me that we haven’t explored the types of statistics that exist. Fun stuff, right? Not exactly and I absolutely recognize that, but understanding fundamental concepts like this can give you the ability to read and talk about statistics with confidence. This confidence is particularly imperative to build during this age of data. So let’s talk basic statistical concepts. I could go any number of directions with that, but if we’re going to start anywhere we should start with explaining descriptive and inferential statistics and the differences between the two.
So let’s talk basic statistical concepts. I could go any number of directions with that, but if we’re going to start anywhere we should start with explaining descriptive and inferential statistics and the differences between the two.
The ins-and-outs of descriptive and inferential statistics could easily take up an entire page, but I don’t plan to go into that much detail other than providing what you need to know and explaining it in layman’s terms.
|Descriptive Statistics||Inferential Statistics|
|What is it?||Statistics that summarize or describe simple, but key characteristics or variables observed in a population.||Statistics based on a sample of an observed population from which inferences can be made about that population.|
|How is it useful?||It simplifies raw, observed data points into understandable and meaningful information about a population’s characteristics.||It allows us to hypothesize and generalize about a population’s characteristics.|
|How is it different than the other?||A descriptive statistic does not state anything beyond the observed data points of a specific characteristic.||An inferential statistic takes an extra step beyond a descriptive statistic and reasons an assumption based on the data and compared to other data.|
|Here’s an example.||The median number of homeless persons in shelters across the U.S. was 1,968 in 2018.||The median number of sheltered homeless persons across U.S. states was 2,317 in 2007 and 1,968 in 2018. We can therefore infer that sheltered homeless populations dropped by 15 percent on average. However, we cannot reason or explain why it may be falling without first bringing in other data as well.|
Source: Jupp, V. (2006). The SAGE dictionary of social research methods : SAGE Publications, Ltd doi: 10.4135/9780857020116
OK, but when will I actually come across descriptive or inferential statistics in the real-world?
You always come across these in the real-world! The statistics and data you see cited in newspaper and television news headlines, journal articles, commercial advertisements, and so on can almost always be categorized as either descriptive or inferential. Does it make a big difference if the creator or author doesn’t tell you whether they are using descriptive or inferential statistics? No, not really; however, it’s important that you recognize when someone is providing the raw, descriptive statistics or extrapolated, inferred statistics based on his or her reasoning. This will reinforces your critical reading of statistics and therefore your confidence in understanding statistics!