In This Chapter
Working with probability
Dealing with random variables and their distributions
Focusing on the binomial distribution
Throughout this book, I toss around the concept of probability, because it's the basis of hypothesis testing and inferential statistics. Most of the time, I represent probability as the proportion of area under part of a distribution. For example, the probability of a Type I error (a/k/a α) is the area in a tail of the standard normal distribution or the t distribution.
In this chapter, I explore probability in greater detail, including random variables, permutations, and combinations. I examine probability's fundamentals and applications, zero in on a couple of specific probability distributions, and I discuss probability-related Excel worksheet functions.
Most of us have an intuitive idea about what probability is all about. Toss a fair coin, and you have a 50-50 chance it comes up "Head." Toss a fair die (one of a pair of dice) and you have a one-in-six chance it comes up "2."
If you wanted to be more formal in your definition, you'd most likely say something about all the possible things that could happen, and the proportion of those things you care about. Two things can happen when you toss a coin, and if you only care about one of them (Head), the probability of that event happening is one out of two. Six things can happen when you toss a die, and if you only care about one ...