If you look back at the previous windows it should be obvious
to you that the sample means changed as you collected data. After collecting
a great quantity of data the sample mean won't change much at all when you
add a few more data points. This is because your sample mean is approaching
the true mean *µ* of the *population* of all possible data.
The sample mean is
just an *estimate* of the true mean of the population.

We refer to data as *parametric* when the population is
normally distributed. With parametric data we can make some useful predictions.
For example, 68.2% of the time the next data point you collect will fall in
the central range of the distribution curve, *within ± one standard
deviation of the sample mean*. A data point will fall within *two* standard
deviations a whopping 95.4% of the time. Another way of stating this fact is
to say that the *probability* of a data point falling within
± two standard deviations of the sample mean is > 0.95. The probability
that the true mean is within two standard deviations of the sample mean is
also > 0.95. Recall that a probability is a number greater than or equal to
zero and less than or equal to 1. A probability of 0.5 corresponds to a 50:50
chance, for example.

What does all of this mean in terms of the difference between our growth rates?