Back when I was in university, there was a 2nd-year statistics course. It was the introductory stats course and was required for many degrees. Of course, being in math, I had to take it. However, I recall that business students, as well as social science students, commonly took it as well. I rarely had those people in my classes; that stats course was the exception.<\/p>\n
I was told that the class had a 50 percent failure\/dropout rate – the highest of all the courses at the university. It was even higher than calculus, which had a 40 percent non-completion rate. However, the stats course did have the enrollment of many students who weren’t math-oriented.<\/p>\n
A common sentiment expressed by statistics students is that it’s hard to apply the ideas to everyday life. In a way, the feeling is ironic, since the first couple of courses in statistics – much more than most math courses – focus on everyday applications.<\/p>\n
Introductory statistics focuses closely on the mean and the standard deviation of a population. The mean is another name for the average; most people understand it as the “expected” value. Consider mean height, for instance. If you imagine a person you can’t see, but want to guess their height, your best guess is the mean height of the population.<\/p>\n
Standard deviation is harder to understand for most people. It’s the measure of how far apart the population’s values are – how spread out they are. Thinking of heights again: in a population with low standard deviation, people’s heights would mainly be close to the same. In a population with high standard deviation, the heights would likely be quite different from person to person.<\/p>\n
I’ll be discussing more technical aspects of mean and standard deviation in coming posts. This, I hope, might be a good first step:)<\/p>\n