

Probability and Statistics Probability and Statistics are not one and the same. The differences are not nuanced. They are Apples and Oranges. Engineers know that stress and strain are not synonymous: they don't mean the same thing, even though the popular press uses the terms interchangeably. (Stress is a force acting over a unit area. Strain is the elongation per unit of original length. One can be viewed as causing the other, and in many instances stress = proportionality constant x strain.) Probability and Statistics are not the same either. They are related, but much more circuitously than as Hooke's Law (above) relates stress with strain. Probability can be viewed either as the longrun frequency of occurrence or as a measure of the plausibility of an event given incomplete knowledge  but not both. Statistics are functions of the observations (data) that often have useful and even surprising properties. So what?
The sample mean,, is a statistic; the population mean,
,is not.
That is because a statistic is observable, being computed from the
observations, while a population parameter, being a philosophical
abstraction, is not observable, and thus must be estimated. Statistics, like
, are often used to
The population parameters are required to estimate probabilities, based on a probability density function, pdf (or probability mass function, pmf, if X is a discrete random variable). So (finally) we see the relationship between probability and statistics:
(With convoluted thought processes like this is it any wonder that statistics is not everyone's favorite subject?) Caveat: Notice that estimating the population parameters is only half the battle. The density from which the observations were taken must also be known. For example, given these observations, what is the probability of a new observation being less than zero? X: 0.10, 0.16, 0.23, 0.32, 0.43, 0.62, 1.0 If you estimate the mean and standard deviation in the usual way, and if you assume that the observations are from a normal density, you would compute that the probability is p=0.1 that a new observation would be less than zero. (If you were paying attention to the very small sample size and used the t density, rather than the normal, you would have p=0.12.) But these observations are not from a normal density, rather they are lognormal, something that a quantilequantile plot would have suggested. Thus the probability of a future observation being less than zero, is p=0, because the lognormal density is defined only for X > 0, since  < log(X) < . Summary: In statistics, as with engineering, pay attention to the fine print.

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