# Define Hypothesis testing and explain test of significance for small samples and large samples.

Define Hypothesis testing and explain test of significance for small samples and large samples.
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Step 1
Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample.
It is used to assess the plausibility of a hypothesis by using sample data.
Step 2
Large sample:-
The sample size n is greater than $30\left(n⇒30\right)$ is known as large sample size. For large samples we use the sampling distributions of statistic are normal or Z test.
Small sample:-
If the sample size n is less than $30\left(n<30\right)$ is known as small sample. For small samples we use the t, F and ${\chi }^{2}$ sampling distributions.
Test of Significance:-
The theory of test of significance consists of various test Statistic. The theory had been developed under two broad heading
Test of significance for large sample
Large sample test or Asymptotic test or Z test $\left(n⇒30\right)$
Test of significance for small samples $\left(n<30\right)$
Small sample test or Exact test-t, F and ${\chi }^{2}$.
It may be noted that small sample tests can be used in case of large samples also.