Establishing the type of distribution, sample size, and known or unknown standard deviation can help you figure out how to go about a hypothesis test. However, there are several other factors you should consider when working out a hypothesis test.
Rare Events
Suppose you make an assumption about a property of the population (this assumption is the null hypothesis). Then you gather sample data randomly. If the sample has properties that would be very unlikely to occur if the assumption is true, then you would conclude that your assumption about the population is probably incorrect. (Remember that your assumption is just an assumption—it is not a fact and it may or may not be true. But your sample data are real and the data are showing you a fact that seems to contradict your assumption.)
Using the Sample to Test the Null Hypothesis
Use the sample data to calculate the actual probability of getting the test result, called the pvalue. The pvalue is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large pvalue calculated from the data indicates that we should not reject the null hypothesis. The smaller the pvalue, the more unlikely the outcome, and the stronger the evidence is against the null hypothesis. We would reject the null hypothesis if the evidence is strongly against it.
Draw a graph that shows the pvalue. The hypothesis test is easier to perform if you use a graph because you see the problem more clearly.
Example 1
A baker bakes 10 loaves of bread. The mean height of the sample loaves is 17 cm. The baker knows from baking hundreds of loaves of bread that the standard deviation for the height is 0.5 cm. The distribution of heights is normal. He claims that his bread height is more than 15 cm, on average. Several of his customers do not believe him.
To persuade his customers that he is right, the baker decides to do a hypothesis test.
Solution:
Since the baker knows the standard deviation from baking hundreds of loaves of bread, we will run Normal ZTest.
The null hypothesis could be H_{0}: μ ≤ 15.
The alternate hypothesis is H_{a}: μ > 15.
The words “is more than” translates as a “>” so “μ > 15″ goes into the alternate hypothesis.
The null hypothesis must contradict the alternate hypothesis.
Since σ is known (σ = 0.5 cm.), the distribution for the population is known to be normal with
 mean μ = 15 and
 standard deviation [latex]\displaystyle\frac{\sigma}{\sqrt{n}}=\frac{0.5}{\sqrt{10}}=0.16[/latex]
The pvalue, then, is the probability that a sample mean is the same or greater than 17 cm. when the population mean is, in fact, 15 cm. We can calculate this probability using the normal distribution for means.
pvalue
= P([latex]\overline{X}[/latex] > 17)
= P([latex]\frac{\overline{X}{\mu}}{\frac{\sigma}{\sqrt{n}}}[/latex] > [latex]\frac{17  {\mu}}{\frac{\sigma}{\sqrt{n}}}[/latex])
= P([latex]\frac{\overline{X}{\mu}}{\frac{\sigma}{\sqrt{n}}}[/latex] > [latex]\frac{1715}{\frac{0.5}{\sqrt{10}}}[/latex])
= P( Z > 12.64911 ) which is approximately zero.
A pvalue of approximately zero tells us that it is highly unlikely that a loaf of bread rises no more than 15 cm, on average.
That is, almost 100% of all loaves of bread would be at least as high as 17 cm. purely by CHANCE had the population mean height really been 15 cm.
Because the outcome of 17 cm is so unlikely to happen (meaning it is happening NOT by chance alone), we conclude that the evidence is strongly against the null hypothesis (the mean height is at most 15 cm.).
There is sufficient evidence that the true mean height for the population of the baker’s loaves of bread is greater than 15 cm.
Using TI83/84:
The result: Interpretation of the result:The zscore of height 17cm is 12.64911. The blue shaded area of Figure 1, also known as pvalue, is 5.854831 * 10^{37}. 
Try It
A normal distribution has a standard deviation of 1. We want to verify a claim that the mean is greater than 12. A sample of 36 is taken with a sample mean of 12.5.
H_{0}: μ ≤ 12
H_{a}: μ > 12
The pvalue is 0.0013
Draw a graph that shows the pvalue.
Decision and Conclusion
A systematic way to make a decision of whether to reject or not reject the null hypothesis is to compare the pvalue and a preset or preconceived α (also called a “significance level”). A preset α is the probability of a Type I error (rejecting the null hypothesis when the null hypothesis is true). It may or may not be given to you at the beginning of the problem.
When you make a decision to reject or not reject H_{0}, do as follows:

Conclusion: After you make your decision, write a thoughtful conclusion about the hypotheses in terms of the given problem.
Example 2
When using the pvalue to evaluate a hypothesis test, it is sometimes useful to use the following memory device
If the pvalue is low, the null must go.
If the pvalue is high, the null must fly.
This memory aid relates a pvalue less than the established alpha (the p is low) as rejecting the null hypothesis and, likewise, relates a pvalue higher than the established alpha (the p is high) as not rejecting the null hypothesis.
Solution:
Reject the null hypothesis when ______________________________________.
The results of the sample data _____________________________________.
Do not reject the null when hypothesis when __________________________________________.
The results of the sample data ____________________________________________.
Try It
CuteBaby Genetics Labs claim their procedures improve the chances of a boy being born. The results for a test of a single population proportion are as follows:
H_{0}: p = 0.50, H_{a}: p > 0.50
α = 0.01
pvalue = 0.025
Interpret the results and state a conclusion in simple, nontechnical terms.