Alternative hypothesis

Alternative hypothesis

An alternative hypothesis is a key concept in statistical hypothesis testing. It is a statement that contradicts the null hypothesis and proposes that there is a statistically significant effect or relationship between variables in a study. The alternative hypothesis is denoted as H₁ or H_a.

Formulation

In the context of hypothesis testing, researchers formulate the alternative hypothesis to test against the null hypothesis (H₀). The null hypothesis typically states that there is no effect or no difference, while the alternative hypothesis suggests the presence of an effect or difference. For example, in a clinical trial, the null hypothesis might state that a new drug has no effect on a disease, whereas the alternative hypothesis would state that the drug does have an effect.

Types of Alternative Hypotheses

There are two main types of alternative hypotheses:

• One-tailed alternative hypothesis: This hypothesis specifies the direction of the effect or relationship. For example, it might state that a new teaching method is better than the traditional method.
• Two-tailed alternative hypothesis: This hypothesis does not specify the direction of the effect or relationship. It only states that there is a difference. For example, it might state that there is a difference in test scores between two teaching methods, without specifying which one is better.

Testing the Alternative Hypothesis

To test the alternative hypothesis, researchers use various statistical tests such as the t-test, chi-square test, and ANOVA. The choice of test depends on the type of data and the research design. The result of the test will either lead to the rejection of the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis.

Significance Level and P-Value

The significance level (α) is a threshold set by the researcher, commonly at 0.05, which determines the probability of rejecting the null hypothesis when it is actually true. The p-value is the probability of obtaining the observed data, or something more extreme, assuming the null hypothesis is true. If the p-value is less than the significance level, the null hypothesis is rejected, supporting the alternative hypothesis.

Related Concepts

• Null hypothesis
• Statistical significance
• P-value
• Type I and Type II errors
• Confidence interval

See Also

• Hypothesis testing
• Statistical inference
• Research design
• Experimental design