Statistics

  In this article, we're going to start our two-part unit on data visualization. Up to this point we've discussed raw data which are just numbers but usually, it's much more useful to represent this information with charts and graphs. There are two types of data we encounter, categorical and quantitative data, and they likewise require different types of visualizations. Today we'll focus on: a.      bar charts b.     pie charts c.      pictographs d.     histograms  and show you what they can and cannot tell us about their underlying data as well as some of the ways they can be misused to misinform. Data visualizations are important to understand  because you’ll see them every day.   In the news,  on Facebook, and  in magazines.   Maybe I’ll make  an infographic of all  the places  we see data visualizations.               ...

Here we discuss measures of spread, or dispersion, which we use to understand how well medians and means represent the data, and how reliable our conclusions are. They can help understand test scores, income inequality, spot stock bubbles, and plan gambling junkets. They're pretty useful, and now you're going to know how to calculate them.   Here we’re heading to the data on both sides of that middle. What statisticians call “measures of spread”. Statistical measures of spread or dispersion tell us how data is spread around the middle. That lets us know how well the mean or median represents the data. And how much we trust conclusions based on the mean and median. And how can we use it in our real life. Measures of spread are all around. From test scores. Like when you find out you scored in the 99th percentile on the LSAT! Economists use measures of spread to study income inequality.   Investors use them to try to identify price bubbles. Gamblers use the...

  Measures of central tendency are the numbers that tend to hang out in the middle of our data: the mean, the median, and mode. All of these numbers can be called “averages” and they’re the numbers we tend to see most often - whether it’s in politics when talking about polling or income equality to batting averages in baseball (and cricket) and Amazon reviews. Averages are everywhere so today we’re going to discuss how these measures differ, how their relationship with one another can tell us a lot about the underlying data, and how they are sometimes used to mislead.       Here we discuss about less showy numbers. The numbers stuck in the middle. ·         The averages. ·         The medians. ·         The modes. They may not seem as mind-blowing. Or all that flashy. But, turns out they are really, really important. Those middle numbers are ofte...