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Related Concept Videos

Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

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Null but not void: considerations for hypothesis testing.

Pamela A Shaw1, Michael A Proschan

  • 1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD 2092-7609, USA. shawpa@niaid.nih.gov

Statistics in Medicine
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

Standard statistical theory offers clear test choices once hypotheses are set. However, selecting appropriate null and alternative hypotheses requires careful consideration of the scientific question and realistic alternatives, complicating test selection.

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Area of Science:

  • Statistics
  • Scientific Methodology

Background:

  • Standard statistical theory provides a framework for hypothesis testing once null and alternative hypotheses are defined.
  • However, traditional statistical education often overlooks the critical process of selecting appropriate null and alternative hypotheses aligned with scientific inquiry.

Purpose of the Study:

  • To highlight the complexities and nuances in choosing null and alternative hypotheses for statistical testing.
  • To demonstrate how realistic alternative hypotheses can necessitate different null hypothesis formulations.
  • To illustrate that practical statistical problems often deviate from textbook simplicity.

Main Methods:

  • The study reviews fundamental principles of statistical hypothesis testing.
  • It presents illustrative examples of hypothesis testing scenarios where the choice of hypotheses is not straightforward.
  • The analysis emphasizes the impact of considering realistic alternatives on hypothesis definition.

Main Results:

  • The selection of null and alternative hypotheses is a critical, often underestimated, step in statistical practice.
  • The choice of hypothesis formulation can significantly influence the selection of the appropriate statistical test.
  • Real-world scientific questions may require non-standard approaches to hypothesis definition.

Conclusions:

  • Choosing the right hypothesis test is less straightforward than standard statistical theory suggests.
  • A deeper consideration of the scientific question and potential alternatives is crucial for robust hypothesis testing.
  • Statistical practice demands a more nuanced approach to hypothesis formulation than often presented in introductory texts.