Hypothesis Testing: Procedure, Steps, and Errors

Running head: HYPOTHESIS TESTING
HYPOTHESIS TESTING
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1HYPOTHESIS TESTING
Introduction
A hypothesis is considered to be a proposition that is in a form which can be tested
and after being tested is capable of predicting a significant relationship in between two or
more than two variables. It can be said that before a researcher comes to the conclusion that a
relationship exists between any two or more than two variables he states it in a form of
hypothesis and then this is tested to prove the same. Hypothesis testing is a procedure that is
done on the basis of sample evidence and the probability theory and is a method that
determines whether a considered hypothesis is true or false (Bonett & Wright, 2015). A
hypothesis can be proven to be true or disproven by making use of the valid data. The paper
will be discussing on the hypothesis testing and elaborating on the step by step procedure
involved in doing the same.
5 steps that are involved in hypothesis testing
1. Stating null and alternate hypothesis
Null hypothesis can be considered as the statement that provides idea for value
parameter of population while alternate hypothesis is the statement which is automatically
accepted if null hypothesis is proven irrational (Mertler & Reinhart, 2016).
2. Selecting the appropriate statistics of the test and the associated level of significance.
In the process of hypothesis testing related to a certain proposition, the z-statistic is
made use of and the formula used in this case is as follows:
z=(p ̂  − p)/(√( pq /n))
While testing a hypothesis of a value that is the mean of the collected data the z-statistics is
used or the t-statistics is also used sometimes and this depends on certain conditions

2HYPOTHESIS TESTING
(Schneider, 2015). In case the standard deviation (σ) of the population is known and if any
one of the conditions are fulfilled that is the recorded data is distributed in a normal basis or
the size of the sample of the data that is denoted by n and is less than 30 then z-statistics is
used. In case when σ is not known and the recorded data is normally distributed or the related
size of the sample is more than 30, then t-distribution is used.
3. Stating the rules of decision making
The rules of decision making are there so that the associated conditions can be stated
based on this null hypothesis will be accepted or declined. Level of significance is there to
determine the associated critical value of test-statistic and critical value is defined as that
value which divides non-rejected region from rejection region.
4. Computing test statistic and taking the decision accordingly
When z-statistics is used, following formula is used:
z=(x−μ)/(σ / √ n)
When t-statistics is used, following formula is used:
t=(x−μ)/( s
√n )
After all of the above calculations are made the test statistic is computed and the critical value
is compared and if in case the value that has been computed comes within the range of
rejection, null hypothesis is not accepted.
5. Interpreting decision- The conclusion to the hypothesis is drawn in this step on the basis
of the decision that has been taken in the step 4.
Null and Research hypothesis
Null hypothesis is considered as a generalised statement and in a null hypothesis there
is exists no link in between the measured phenomena or association among the groups. A