Unit 8: Sampling Distribution
Differentiate between descriptive and inferential statistics
Concept of a sampling distribution
Central Limit Theorem and its importance
Mean and standard deviation for the
sampling distribution of the sample mean
Descriptive statistics
-Collecting, presenting, and describing data either for population or simple information.
Inferential statistics
-Drawing conclusions and/or making decisions concerning a population based only on sample data.
Defined as the process of selecting some observations (subjects) from all the observations (subjects) from a particular group or population.
Properties:
As the sample size gets large enough
The sampling distribution becomes almost normal regardless of shape of population
The mean of the sampling distribution will equal the mean of the population
Sampling distribution of X is approximately normal distributed if:
the population is normally distributed, or
sample size > or equal to 30 through CLT through the shape of population is not normal
Z-value for the sampling distribution of X
x= sample mean
u= population mean
o= standard error of the mean
Sampling Distribution of Sample Mean
Why Sample?
High precision
Less costly
Less time consuming
Sampling theory is a study of the relationship that exist between a population and samples drawn from the population.
provide a logical basis for
using samples to make inferences about populations
Sampling theory
Used in determining whether observed differences between two samples are actually due to chance variation or whether they are significant