00:00
03:39
11:23
Crash Course: Statistics
Normal, Unimodal Distribution
03:39 - 05:06
Explains a "normal" distribution and its components.
Sign up to access lesson features such as sample questions, standards alignment, and discussion tools to check for understanding.
Comments
Please sign in to write a comment.
Video Transcript
3:38
with the normal distribution.
3:39
We mentioned the Normal distribution when we talked about the different ways to measure
3:42
the center of data--since the mean, median, and mode of a normal distribution are the
3:46
same.
3:47
This tells us that the distribution is symmetric, meaning you could fold it in half and those
3:52
halves would be the same and that it’s unimodal, meaning there’s only one peak.
3:56
The shape of a normal distribution is set by two familiar statistics: the mean and standard
4:01
deviation.
4:02
The mean tells us where the center of the distribution is.
4:05
The standard deviation tells us how thin or squished the normal distribution is.
4:10
Since the standard deviation is the average distance between any point and the mean, the
4:15
smaller it is the closer all the data will be to the mean.
4:18
We’ll have a skinnier normal distribution.
4:20
Most of the data in the normal distribution--about 68%--is within 1 standard deviation of the
4:26
mean on either side.
4:28
Just like the quartiles in a boxplot, the smaller the range that 68% of the data has
4:33
to occupy, the more squished it gets.
4:35
Speaking of boxplots here’s what the boxplot for normally distributed data looks like.
4:40
The two halves of our box are exactly the same because the normal distribution is symmetric.
4:45
You’ve probably seen the normal distribution in a lot of different places, it gets called
4:48
a Bell Curve sometimes.
4:50
Attributes like IQ and the number of Fruit Loops you get in a box are approximately normally
4:54
distributed.
4:55
Normal distributions come up a lot when we look at groups of things, like the total value
5:00
rolled after 10 dice rolls, or birth weights.
5:02
We’ll talk more about why the normal distribution is so useful in the future.
