1. Sampling fundamentals

2. Concepts
Sampling process
Sample
Universe/ population
Need for sampling
Sampling error

3. Statistic and Parameter
A statistic is a characteristics of a sample , whereas
a parameter is a characteristic of a population.
Thus, when we work out certain measures such as
mean, median, mode, standard deviation from
sample, they are called statistic as they will describe
the characteristic of the population.
Eg Sample mean= x . Sample s.d. = s
Eg of parameter , population mean= μ ,population s.d. = σp

4. Formula
Sample Mean Formula: x =∑x
n
Sample variance Formula s2= ∑(X-X)2
(n-1)
n= sample size
Population Mean Formula: μ =∑x
N
Population variance Formula σ2= ∑(X-X)2
N
N= population size.

5. The law of large numbers
Draw observations at random from any population
with finite mean μ. As the number of observations
drawn increases, the mean of the observed
values gets closer and closer to the mean μ of the
population. `
x

6. The central limit theorem
Take a large (30 or more) random sample of size n
from any population with mean μ and standard
deviation σ. The sample mean, X is approaches the
normal distribution with mean μ and standard
deviation . n
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n
NX
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