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Related Concept Videos

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...
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...
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.
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...
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.
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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Related Experiment Video

Updated: Jul 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Stopping Rule for the MLE Algorithm Based on Statistical Hypothesis Testing.

E Veklerov, J Llacer

    IEEE Transactions on Medical Imaging
    |January 1, 1987
    PubMed
    Summary
    This summary is machine-generated.

    A new stopping rule for the maximum likelihood estimator (MLE) algorithm prevents image deterioration. This quantitative criterion, based on a statistical hypothesis test, stops the iterative reconstruction before image quality degrades, improving results in low-activity regions.

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    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

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    Last Updated: Jul 7, 2026

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    Area of Science:

    • Medical Imaging
    • Image Reconstruction
    • Statistical Modeling

    Background:

    • Maximum Likelihood Estimator (MLE) algorithms are widely used for image reconstruction.
    • MLE algorithms can produce image deterioration beyond a certain iteration point.
    • The Poisson distribution assumption underlies MLE, but can conflict with optimal image generation.

    Purpose of the Study:

    • To propose a quantitative criterion to determine the optimal stopping point for MLE image reconstruction.
    • To prevent the deterioration of images generated by iterative algorithms.
    • To improve image quality in both high and low activity regions.

    Main Methods:

    • Developed a quantitative criterion with a probabilistic interpretation.
    • Tested a statistical hypothesis to assess image quality at each iteration.
    • Utilized a parameter that decreases with image improvement and increases with deterioration.

    Main Results:

    • The proposed stopping rule effectively halts the MLE algorithm before image deterioration occurs.
    • Images reconstructed using the proposed rule show reduced noise in high-activity regions compared to filtered back-projection.
    • Significant image quality improvements were observed in low-activity regions.

    Conclusions:

    • The developed stopping criterion offers a reliable method to optimize MLE image reconstruction.
    • This approach enhances image fidelity, particularly in areas with low signal.
    • The stopping rule's potential applicability to other iterative reconstruction schemes warrants further investigation.