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Hypothesis testing is a statistical method used to make decisions about population parameters based on sample data. It involves formulating two mutually exclusive hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis typically states that there is no effect or difference, while the alternative hypothesis suggests that there is an effect or difference12.
Key Concepts
Null Hypothesis (H0): This is a general statement or default position that there is no relationship between two measured phenomena. For example, a company's mean production is 50 units per day (H0: ÎĽ = 50).
Alternative Hypothesis (H1): This is the hypothesis that contradicts the null hypothesis. For example, a company's production is not equal to 50 units per day (H1: μ ≠50).
Level of Significance (α): This is the probability of rejecting the null hypothesis when it is true. It is usually set at 0.05 or 5%, meaning there is a 5% risk of concluding that a difference exists when there is no actual difference1.
P-value: This is the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct. If the p-value is less than the significance level, we reject the null hypothesis2.
Test Statistic: This is a standardized value that is calculated from sample data during a hypothesis test. It is used to determine whether to reject the null hypothesis1.
Critical Value: This is the threshold value that the test statistic must exceed to reject the null hypothesis2.
Understanding Hypothesis Testing - GeeksforGeeks
A hypothesis is an assumption or idea, specifically a statistical claim about an unknown population parameter. For example, a judge assumes a person is innocent and verifies this by reviewing evidence and hearing testimony before reaching a verdict. Hypothesis testing is a statistical method that … See more
Hypothesis testing is an important procedure in statistics. Hypothesis testing evaluates two mutually exclusive population … See more
One tailed test focuses on one direction, either greater than or less than a specified value. We use a one-tailed test when there is a clear directional … See more
- Step 1: Define Null and Alternative Hypothesis
- State the null hypothesis (H0H_0H0​), representing no effect, and the alternative hypothesis (H1H_1H1​​), suggesting an ef…
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In hypothesis testing,Type I and Type II errors are two possible errors that researchers can make when drawing conclusions about a population based on a sample of data. These errors are associated with the decisions made regarding the null … See more
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