The alternative hypothesis (H₁) in statistical hypothesis testing stands in opposition to the null hypothesis (H₀). It is the hypothesis that researchers aim to support. If the sample data provide sufficient evidence, it leads to the rejection of the null hypothesis in favor of the alternative hypothesis. This hypothesis is what a researcher typically expects to conclude based on the study or experiment performed.
Examples
Medical Research:
- Null Hypothesis (H₀): The new drug has no effect on patients.
- Alternative Hypothesis (H₁): The new drug lowers blood pressure in patients.
Market Research:
- Null Hypothesis (H₀): Customer satisfaction levels have not changed after introducing the new feature.
- Alternative Hypothesis (H₁): Customer satisfaction levels have improved after introducing the new feature.
Frequently Asked Questions
What is the purpose of the alternative hypothesis?
The alternative hypothesis provides a statement that indicates the existence of an effect or difference. It helps researchers determine if their predictions and theories can be supported by data.
How is the alternative hypothesis represented?
The alternative hypothesis is generally represented by H₁ or H_a.
When is the alternative hypothesis accepted?
The alternative hypothesis is accepted when there is sufficient evidence in the sample data to reject the null hypothesis.
What is the relationship between the null hypothesis and the alternative hypothesis?
The null hypothesis and the alternative hypothesis are mutually exclusive and collectively exhaustive. Acceptance of the alternative hypothesis implies the rejection of the null hypothesis, and vice versa.
Can the alternative hypothesis predict a specific direction?
Yes, alternative hypotheses can be one-sided (e.g., specifying a particular direction such as “greater than”) or two-sided (e.g., indicating a difference without specifying the direction).
Related Terms
- Null Hypothesis (H₀): The hypothesis that there is no significant difference or effect.
- p-value: Measures the probability of observing the sample data, or something more extreme, assuming the null hypothesis is true.
- Type I Error: The rejection of a true null hypothesis (false positive).
- Type II Error: The failure to reject a false null hypothesis (false negative).
- Statistical Significance: The likelihood that the observed association or effect is not due to chance.
Online References
Suggested Books for Further Studies
- “Introduction to the Practice of Statistics” by David S. Moore, George P. McCabe, and Bruce A. Craig
- “The Art of Statistics: How to Learn from Data” by David Spiegelhalter
- “Hypothesis Testing Made Simple” by De Haan B
Fundamentals of Alternative Hypothesis: Statistics Basics Quiz
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