Inferential statistics uses analytical tools to draw conclusions about populations from samples. Some common inferential statistics tests include the t-test, which compares means between two groups; correlation coefficients, which measure the strength and direction of relationships between variables; analysis of variance (ANOVA) tests, which compare means across multiple categorical groups; and chi-square tests, which measure how observed data matches a theoretical model. Researchers preset a level of significance, such as 0.01 or 0.05, to determine whether results reject the null hypothesis and are statistically significant.

1. Inferential statistics
➢ is a branch of statistics that makes the use of various analytical
tools to draw inferences about the population data from sample
data.
T-test:
➢ is a statistical test that is used to compare the means of two
groups. It is often used in hypothesis testing to determine
whether a process or treatment actually has an effect on the
population of interest, or whether two groups are different from
one another.
Correlation coefficient :
➢ is a statistical measure of the strength of a linear
relationship between two variables.
• The Pearson correlation coefficient (r) is the most common way of
measuring a linear correlation. It is a number between –1 and 1 that
measures the strength and direction of the relationship between two
variables.
• The Spearman correlation between two variables is equal to
the Pearson correlation between the rank values of those two
variables; while Pearson's correlation assesses linear
relationships.
Analysis of Variance(ANOVA):
➢ An ANOVA test is a type of statistical test used to determine if
there is a statistically significant difference between two or more
categorical groups by testing for differences of means using
variance.
CHI – Square:
➢ chi-square (χ2
) statistic is a test that measures how a model
compares to actual observed data.
Level of Significance:
➢ The level of significance is stated to be the probability of type I
error and is preset by the researcher with the outcomes of error.
The level of significance is the measurement of the statistical
significance. It defines whether the null hypothesis is assumed
to be accepted or rejected. It is expected to identify if the result
is statistically significant for the null hypothesis to be false or
rejected. ( .01 or .05 level of significance).