Summary – Null Hypothesis and Alternative Hypothesis
The null hypothesis (H0) and alternative hypothesis (Ha) are fundamental concepts in statistical hypothesis testing. The null hypothesis represents the default assumption, stating that there is no significant effect, difference, or relationship between variables. It serves as the baseline against which the alternative hypothesis is tested. In contrast, the alternative hypothesis represents the researcher’s hypothesis or the claim to be tested, suggesting that there is a significant effect, difference, or relationship between variables. The relationship between the null and alternative hypotheses is such that they are complementary, and statistical tests are conducted to determine whether the evidence from the data is strong enough to reject the null hypothesis in favor of the alternative hypothesis. This decision is based on the strength of the evidence and the chosen level of significance. Ultimately, the choice between the null and alternative hypotheses depends on the specific research question and the direction of the effect being investigated.
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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