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Related Concept Videos

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.
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
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.

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Related Experiment Video

Updated: Jun 12, 2026

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

Prospective detection of large prediction errors: a hypothesis testing approach.

Dan Ruan1

  • 1Department of Radiation Oncology, Stanford University, Stanford, CA, USA. druan@stanford.edu

Physics in Medicine and Biology
|June 24, 2010
PubMed
Summary

This study introduces a hypothesis testing method to detect potential large prediction errors in real-time tumor motion for radiotherapy. It helps identify when to pause treatment for improved accuracy and safety.

Related Experiment Videos

Last Updated: Jun 12, 2026

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 Physics
  • Radiotherapy
  • Computational Biology

Background:

  • Real-time motion management is crucial in radiotherapy for synchronizing treatment with tumor motion.
  • System latency necessitates accurate tumor motion prediction to compensate for delays.
  • Identifying instances of potential large prediction errors is critical for safety and treatment adjustments.

Purpose of the Study:

  • To develop a real-time hypothesis testing approach for detecting potentially large prediction errors in tumor motion.
  • To provide a method for automatically adjusting radiotherapy treatment or monitoring schemes based on prediction accuracy.

Main Methods:

  • A hypothesis testing framework was developed, treating future tumor location as a random variable.
  • Kernel density estimation was used to obtain the empirical probability distribution of tumor location.
  • A likelihood ratio test (LRT) was derived, simplifying to a variance test of the predictive random variable.

Main Results:

  • The proposed method effectively identifies instants with potentially large prediction errors by assessing the uncertainty in predictions.
  • Performance was evaluated using patient-derived respiratory traces, with receiver operating characteristic (ROC) curves detailing detection accuracy.
  • Analysis of miss detection rate and delivery efficiency demonstrated the method's clinical promise.

Conclusions:

  • The study presents a novel real-time method for analyzing prediction accuracy in tumor motion management.
  • This approach offers critical insights for automatically adjusting radiotherapy delivery and target monitoring.
  • The findings support enhanced safety and efficacy in image-guided radiotherapy through adaptive prediction error detection.