What is Inferential Statistics?

If you need to collect data on a very large

population. For example, suppose you want to know the average height of all the

men in a city with a large population, it isn’t very practical to try and get

the height of each man. Inferential statistics is used instead.

Inferential statistics makes inferences

about populations using data drawn from the population. The statistician will

collect a sample or samples from the millions of residents and make inferences

about the entire population using the sample. While carrying out Inferential

statistics upon a sample, it automatically obtains a sampling error and thus a

sample is not expected to acquire a 100% accurate representation of the

population. In addition, it is used by statisticians

to carry out estimates and test a theory by its given data.

Types of

Inferential Statistics

1) Correlation Coefficient

It shows the relationship

between two quantitative variables and is symbolized using the letter ‘r’. The

range of ‘r’ is from 1 (perfect, positive, linear relationship) to -1

(perfect, negative, linear relationship). No linear relationship is when r = 0.

The closer the coefficient is to +1, the stronger the positive relationship. The

closer the coefficient is to -1, the stronger the negative relationship. If the

coefficient is nearer 0, the variables are not linearly related to each other,

although they may be non-linearly related. A correlation coefficient of 0

represents the weakest possible relationship, although it is still possible a

non-linear relationship may exist when r = 0.

When one variable increases, the other variable

increases or decreases by giving a straight-line graph. However, if the line

increases to a certain point and then decrease, a curvilinear or non-linear

relationship would exist.

2) t-test

The Student’s t-test is a statistical test

that compares the mean and standard deviation of two samples to

see if there is a significant difference between them. In an

experiment, a t-test is used to test if there is a significant difference

between the two variables. While taking a sample from a population, an error may

be observed known as sampling error.

In any significance test,

there are two possible hypothesis:

i) Null

Hypothesis:

There is no significant difference between the two variables

and the difference is due to chance or sampling error.

ii)

Alternative Hypothesis:

There is a significant difference between the two variables

and the difference is neither due to chance nor sampling error.

3) Analysis

of Variance (ANOVA)

ANOVA is similar to the t-test, but it is used to

compare two or more means and whether there is a significance between them. They help you to figure out if you need to reject the null hypothesis or accept the alternate hypothesis.

4) Analysis

of Covariance (ANCOVA)

Analysis

of covariance (ANCOVA) is used in examining the differences in the mean values

of the dependent variables that are related to the effect of the controlled

independent variables while taking into account the influence of the

uncontrolled independent variables.

5) Chi-Square

This type of test to see if there is

a relationship between two qualitative variables, such as sex and dropping out

of school.

Importance of Inferential Statistics

Inferential

statistics enables us to make conclusions from descriptive statistics. Data obtained

from descriptive statistics are used by inferential statistics extend beyond

the immediate data. Inferential statistics are used to interpret from the

sample data what the population might think. This type of statistics can also be

used to know that if the observation is reliable and dependent or it has occurred

by chance during the experiment.