Inferential Statistics

[Name of the Author]

[Name of the Institute]

Inferential Statistics1

Limitations and applications of statistical hypothesis testing1

Sampling Methodologies1

Stratified Random Sampling1

SRS2

Cluster Sampling2

Systematic Random Sampling2

Correlations3

Positive Correlation3

Negative correlation3

Minimal4

GROUP A4

If Positive Correlation4

If Negative Correlation4

If Minimal Correlation:4

GROUP B4

If Positive Correlation4

If Negative Correlation5

If Minimal Correlation:5

GROUP C5

If Positive Correlation5

If Negative Correlation5

If Minimal Correlation:5

GROUP D6

If Positive Correlation6

If Negative Correlation6

If Minimal Correlation:6

References7

Inferential Statistics

Limitations and applications of statistical hypothesis testing

There are two main limitations of hypothesis testing. The first is related to the sampling. In inferential statistics and hypothesis a researcher makes inferential recommendations about a population, and due to sampling the population is not completely studies. Because of this the statistician cannot be completely certain of his results. The second limitation is sometimes considered an extension to the first limitation. The second limitation is that a degree of uncertainty remains because at certain times the statistician has to make guesses on the methodology of the inferential test. These guesses are based on theory. Because of these guesses the complete surety over the certainty of the calculations and results is compromised.

Sampling Methodologies

Below are some of the sampling methodologies that can be applied to business situations.

Stratified Random Sampling

In this type of sampling the population is divided into homogenous subgroups and after that a simple random sample from each group is taken. The sub groups in which the population is divided are called strata.

The stratified random sampling not only represents the whole population but also represents the sub groups within the population through strata. In simple random sampling, a sample from the whole population is taken, but it does not show the exclusivity of subgroups within the population.

SRS

Simple random sampling is the simplest sampling method in statistics.

The simple random sample can be obtained by a variety of mechanical and computerized methods. The major factor for selecting a simple random sample is that the selector (whether a machine or a person) should be blindfolded against the population, there should not be any biasness for a particular value of n, because in a simple random sample every n value has equal probability of occurrence.

Cluster Sampling

Cluster sampling is a sampling technique in which the population is divided into separate groups or cluster. A random sample is selected from these clusters. Finally each observation from the randomly selected clusters is included in the sample.

Cluster sampling is done when the population is widely scattered or when the complete lists of members of population cannot be obtained ...