Interpreting Quantitative Research

Please discuss how both Descriptive and Inferential Statistics are utilized in quantitative research to answer stated research questions. Explain how the data are interpreted using these techniques. And briefly discuss how the output is relayed to the reader (e.g., in forms of figures, tables etc).

Agbada is 4 piece ensemble worn in Western Africa

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Descriptive statistics is when you describe or summarize things that you already know.  You see me in a fly wheat-colored agbada and matching uttari with a lovely buttercream pattern.  You don't really know what I'm wearing, but you like it, so you comment.  I like your dress thingie and matching head scarf thing.

Inferential statistics is when you move on to comparing my outfit to what other Math Teachers are wearing, testing a hypothesis or making a prediction.  I think Malik will wear her Sunday sweatshirt on Thursday, because she's weird like that, and she has worn it the past 3 Thursdays to class.  You take a sample representation (some of my outfits) out of the entire population (all the junk in my closet) and make a comparison or prediction and even test your hypothesis.

Uttari - scarves named after the region in India

The way you interpret the data for these two types of statistics also differs a bit.  For Descriptive Statistics, you can only describe what you see, so you would use mean, media, mode or some sort of measure of central tendency that describes most of your data. For the Inferential statistics, its more like what you want other people to see.  You want them to see this certain relationship, so you kind of mold your data to fit what you want them to see, by using logistic regression analysis, linear regression, analysis of variance (or covariance), statistical significance, correlation analysis, etc. Kinda like only posting the good shit and fancy restaurants and expensive outfits on Instagram, because that’s what you want people to see.

I imagine myself baking elaborate basket shaped loaves (don't ask me why).  But, inferential statistics is just you 'kneading the bread', and shaping it into what you want it to look like.  The way you manipulate it, or best fit the bread mold (no pun intended)... Well, those numbers and how you are 'rearranging the data' are the regression part of the math. The output is relayed to the reader in nice little pictures, graphs, charts and tables.  Basically, the butter on the bread. It makes it palatable to the reader, easy to understand and nice to look at. They never know all the trouble you had with getting it to look like that, behind the scenes.

Hope this helps someone out there, because I realize (sadly) that not everyone likes math, and statistics is just hated even more.

Basket made of bread that imagine myself eating… alone… in a corner somewhere with tea!