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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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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...
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Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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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.
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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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...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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%...
598
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

488
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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What is a Hypothesis?01:14

What is a Hypothesis?

15.0K
A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
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High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow.

Jonathan Ozik1, Nicholson Collier1, Justin M Wozniak1

  • 1Argonne National Laboratory, Argonne, IL, USA.

BMC Bioinformatics
|December 23, 2018
PubMed
Summary

We developed a computational framework integrating PhysiCell and EMEWS for high-throughput cancer modeling. This approach aids in testing hypotheses and understanding therapeutic failure in complex cancer systems.

Keywords:
Agent-based modelCancerEMEWSHigh throughput computingHypothesis testingImmunotherapyPhysiCell

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Area of Science:

  • Computational biology
  • Systems biology
  • Cancer research

Background:

  • Cancer is a complex multiscale system involving tumor-host interactions.
  • Therapeutic outcomes can be unpredictable due to this complexity.
  • Mechanistic computational models are valuable but challenging to explore due to high dimensionality and biological uncertainty.

Purpose of the Study:

  • To develop a computational framework for high-throughput hypothesis testing in cancer.
  • To integrate agent-based modeling with advanced model exploration techniques.
  • To systematically investigate factors influencing cancer treatment efficacy.

Main Methods:

  • Integration of PhysiCell (a 3-D multicellular simulator) with EMEWS (an extreme-scale model exploration platform).
  • Development of a generalized workflow for high-throughput cancer hypothesis testing.
  • Utilizing hundreds or thousands of mechanistic simulations for hypothesis optimization.

Main Results:

  • Demonstration of the PhysiCell-EMEWS framework applied to 3-D cancer immunotherapy.
  • Gained insights into mechanisms of therapeutic failure.
  • Established a workflow for comparing simulations against data-driven error metrics.

Conclusions:

  • Combining mechanistic agent-based models with high-throughput exploration environments enables rapid and systematic cancer research.
  • These computational experiments can deepen biological understanding and guide future research.
  • The approach has the potential to inform clinical practice by improving treatment strategies.