In addition to calculating confidence intervals, hypothesis testing is another way to make statistical inferences. This process involves considering two opposing hypotheses regarding a given data set (referred to as the null hypothesis and the alternative hypothesis). Hypothesis testing determines whether the null hypothesis can be accepted or rejected.
Null and Alternative Hypotheses
The actual test begins by considering two hypotheses. They are called the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
H0, the - null hypothesis: a statement of no difference between sample means or proportions or no difference between a sample mean or proportion and a population mean or proportion. In other words, the difference equals 0.
Ha - , the alternative hypothesis: a claim about the population that is contradictory to H0 and what we conclude when we reject H0.
Since the null and alternative hypotheses are contradictory, you must examine evidence to decide if you have enough evidence to reject the null hypothesis or not. The evidence is in the form of sample data.
After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are reject H0 if the sample information favors the alternative hypothesis or do not reject H0 or decline to reject H0 if the sample information is insufficient to reject the null hypothesis.
Mathematical Symbols Used in H0 and Ha:
H0 | Ha |
---|---|
equal (=) | not equal (≠) or greater than (>) or less than (<) |
greater than or equal to (≥) | less than (<) |
less than or equal to (≤) | more than (>) |
Note
Example 9.1
Try It 9.1
Example 9.2
H0 : μ = 2.0
Try It 9.2
- H0 : μ __ 66
- Ha : μ __ 66
Example 9.3
H0 : μ ≥ 5
Try It 9.3
- H0 : μ __ 45
- Ha : μ __ 45
Example 9.4
H0 : p ≤ 0.066
Try It 9.4
- H0 : p __ 0.40
- Ha : p __ 0.40
Collaborative Exercise
Source: OpenStax, https://openstax.org/books/statistics/pages/9-introduction
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