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Related Concept Videos

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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...
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.
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...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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...

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Some consequences of using the Horsfall-Barratt scale for hypothesis testing.

C H Bock1, T R Gottwald, P E Parker

  • 1United States Department of Agriculture, Agricultural Research Service, Byron, GA 31008, USA. clive.bock@ars.usda.gov

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|September 16, 2010
PubMed
Summary

Nearest percent estimates (NPEs) offer higher statistical power for detecting treatment effects in plant pathology compared to the Horsfall-Barratt (H-B) scale. NPEs reduce the risk of type II errors, especially with accurate raters and limited sample sizes.

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Area of Science:

  • Plant Pathology
  • Statistical Analysis in Biological Sciences

Background:

  • Hypothesis testing is crucial for comparing treatment effects in plant pathology.
  • Disease severity assessment methods can influence statistical outcomes.
  • The Horsfall-Barratt (H-B) scale is a common, but potentially less precise, method for disease severity estimation.

Purpose of the Study:

  • To compare the statistical power of Nearest Percent Estimates (NPEs) versus the Horsfall-Barratt (H-B) scale for hypothesis testing in plant disease assessment.
  • To determine if the assessment method impacts the probability of detecting true treatment effects (rejecting a false null hypothesis).

Main Methods:

  • A simulation model was developed using field-collected data on disease severity (0-60%).
  • The model analyzed the relationships between actual severity, NPEs, and H-B scale data, including their distributions and standard deviations.
  • Hypothetical disease severity populations were compared using t-tests with both NPE and H-B midpoint data.

Main Results:

  • Standard deviations of mean NPEs closely matched original rater data.
  • H-B scale data showed increased standard deviations, particularly between 20-50% severity, widening grade intervals.
  • NPE data demonstrated a higher probability of rejecting a false null hypothesis (H0) compared to H-B scale data.
  • H-B scale data required up to 50% larger sample sizes to achieve the same statistical power as NPEs.
  • Accurate raters using NPEs had greater power to detect true effects than with the H-B scale.

Conclusions:

  • Nearest percent estimates (NPEs) provide more accurate disease severity data, leading to increased statistical power in hypothesis testing.
  • The Horsfall-Barratt (H-B) scale's interval scaling can introduce variance, increasing the risk of type II errors (failing to reject a false H0).
  • NPEs are recommended for more reliable treatment effect comparisons in plant pathology, especially when sample sizes are limited or high precision is needed.