Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples

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  • 0:01 What is a Sampling…
  • 0:45 Why Use a Normal Distribution?
  • 1:19 The Central Limit Theorem
  • 2:04 Protecting the Central…
  • 3:15 Calculating the Z-Score
  • 3:55 Lesson Summary
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Lesson Transcript
Instructor: Kevin Newton

Kevin has edited encyclopedias, taught middle and high school history, and has a master's degree in Islamic law.

Want proof that all of this normal distribution talk actually makes sense? Then you've come to the right place. In this lesson, we look at sampling distributions and the idea of the central limit theorem, a basic component of statistics.

What is a Sampling Distribution?

Statisticians sound pretty sure of themselves when talking about normal distribution. But, what makes them so confident that it works? After all, couldn't there be other sample distributions, the name given to the graphical result of incidences? Look, I understand your skepticism. I'll tell you what, take two dice, roll them, and add the results. If you were a betting woman, I'd say if you did that 10 times, you would get more 5s, 6s, and 7s than anything else. Go ahead, you can press the pause button. I'll be here.

Did it work out like I said? Or was I wrong? If I was wrong, go ahead and do the same thing again another 90 times. Trust me, I'll be waiting.

Why Use a Normal Distribution?

Countless statistics students have expressed the same doubt that some of you may have. Countless other statisticians have used supercomputers to run millions and billions of those operations. What they have come up with is the normal distribution, a roughly bell-shaped distribution that occurs over and over throughout populations and samples. Simply put, when something is staring you back in the face as obvious, statisticians tend not to ignore it, especially when it's as useful as the normal distribution.

The Central Limit Theorem

On many graphs of normal distributions, you'll see that there's a line that runs right through the middle, at the highest point of the curve. This is aptly named the central line, and has a theorem named after it. The central limit theorem states that if you run a random experiment enough times the results will follow a normal distribution. In fact, the central limit theorem also states that the greater the opportunity for deviation amongst the variables, the greater that the final curve will resemble a normal distribution. Adding the results of two dice together will definitely look like a normal distribution, given enough rolls, while adding four or five dice together on each throw will look like a normal distribution much earlier.

Protecting the Central Limit Theorem

Of course, such a regular prediction of data is only useful as long as we can protect it from corruption. Everything must be random. If you were using a set of loaded dice, then chances are your graph looks quite different than mine. The same goes if you were not making sure that each roll of the dice was an honest attempt at randomness.

Across a larger population, we can't always double check every input to make sure that it was free of influence from any other data. However, we can still make special note of cases that exist that would corrupt such data.

Let's say that you had a class of students that was normally distributed in height and randomly selected from the student body at large. If you were going to expand that class from 30 to 40, the data set only maintains integrity if the new students are drawn from a random sampling of students. If, on the other hand, your class is suddenly flooded with members of the basketball team who chose to take the class in particular, the result could change. As basketball players are statistically taller than the rest of the population, your class would no longer have a normal sample.

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