Formula of Null Hypothesis
The Null Hypothesis is typically formulated as a statement of equality or absence of a specific parameter in the population being studied. It provides a clear and testable prediction for comparison with the alternative hypothesis. The formulation of the Null Hypothesis typically follows a concise structure, stating the equality or absence of a specific parameter in the population.
Mean Comparison (Two-sample t-test)
H0: μ1 = μ2
This asserts that there is no significant difference between the means of two populations or groups.
Proportion Comparison
H0: p1 − p2 = 0
This suggests no significant difference in proportions between two populations or conditions.
Equality in Variance (F-test in ANOVA)
H0: σ1 = σ2
This states that there’s no significant difference in variances between groups or populations.
Independence (Chi-square Test of Independence):
H0: Variables are independent
This asserts that there’s no association or relationship between categorical variables.
Null Hypothesis
Null Hypothesis, often denoted as H0, is a foundational concept in statistical hypothesis testing. It represents an assumption that no significant difference, effect, or relationship exists between variables within a population. It serves as a baseline assumption, positing no observed change or effect occurring. The null is the truth or falsity of an idea in analysis.
In this article, we will discuss the null hypothesis in detail, along with some solved examples and questions on the null hypothesis.
Table of Content
- What is Null Hypothesis?
- Null Hypothesis Symbol
- Formula of Null Hypothesis
- Types of Null Hypothesis
- Null Hypothesis Examples
- Principle of Null Hypothesis
- How do you Find Null Hypothesis?
- Null Hypothesis in Statistics
- Null Hypothesis and Alternative Hypothesis
- Null Hypothesis and Alternative Hypothesis Examples
- Null Hypothesis – Practice Problems
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