II. Why is the normal distribution

II. Why is the normal distribution useful? •Many things actually are normally distributed, or very close to it. For example, height and intelligence are approximately normally distributed; measurement errors also often have a normal distribution •The normal distribution is easy to work with mathematically. In many practical cases, the methods developed using normal theory work quite well even when the distribution is not normal. •There is a very strong connection between the size of a sample N and the extent to which a sampling distribution approaches the normal form. Many sampling distributions based on large N can be approximated by the normal distribution even though the population distribution itself is definitely not normal.

II.
Why is the Normal Distribution useful?
•
Norm Many Things are actually
a
lly Distributed, or very close to it. Exam for
PLE, height
and Intelligence are Approxim
a
Norm Tely
a
lly Distributed; M
Easurem
ent Errors also of
Ten
have a Norm
a
Distribution L
•
The Norm
a
L Distribution is Easy to Work with M
a
them
Atically. In M
a
ny practical Cases,
the M
E
Thods developed using Norm
a
L Theory Work quite well even when the Distribution is
not Norm
Al.
•
There is a very strong Connection between the Size of a Sam
P
le N and the extent to which.
a Sam
P
Distribution Ling approaches the Norm
a
L form
. Many Sam
P
Ling distributions based
on Large N Approxim Can be
a
Ted by the Norm
a
Distribution L even though the population
is definitely not Norm Distribution Itself
a
L.

II.
Why is the normal distribution useful?
-
Many things actually are norm

lly, a distributed or very close to it. For. Exam

, ple height and intelligence are approxim



a tely norm a lly distributed; m

ent easurem errors also of

have ten a. Norm


-
a l distribution The norm

l a distribution is easy to work with m


a them atically. In m


cases a ny practical, the. M

thods e developed using a norm

.L theory work quite well even when the distribution is

not norm al.
-
There is a very strong connection between the size. Of a Sam

Le P N and the extent to which


A Sam P Ling distribution approaches the norm


. Many a L form Sam

Ling P distributions. Based
on large N can be approxim

ted a by the norm

l a distribution even though the population
distribution itself is. Definitely not norm

l a.