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Related Concept Videos

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.
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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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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.
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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
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High-Stakes Collaborative Testing: Why Not?

Ruth E Levine1, Nicole J Borges2, Brenda J B Roman3

  • 1a Office of Clinical Education and Department of Psychiatry and Behavioral Sciences , The University of Texas Medical Branch , Galveston , Texas , USA.

Teaching and Learning in Medicine
|December 9, 2017
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Summary
This summary is machine-generated.

High-stakes collaborative tests in medical education are valuable for students, regardless of team formation. This approach offers insights into subject matter, self-awareness, and team dynamics, proving feasible despite initial concerns.

Keywords:
academic performancecollaborative testingemotional intelligenceteam-based learningteamwork

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Area of Science:

  • Medical Education
  • Collaborative Learning
  • Assessment Methods

Background:

  • Research on high-stakes collaborative testing in medical education is limited.
  • Understanding student experiences in both established and newly formed teams is crucial.

Purpose of the Study:

  • To explore the phenomenon of high-stakes collaborative testing in medical education.
  • To examine student experiences within established and newly formed teams during collaborative testing.

Main Methods:

  • Utilized the National Board of Medical Examiners Psychiatry subject test in a 2-stage process.
  • Conducted focus groups with third-year psychiatry students (N=49) across 5 medical schools.
  • Triangulated qualitative findings with quantitative data for robust analysis.

Main Results:

  • Students reported heightened emotions but found collaborative tests valuable, irrespective of team status.
  • Participants described learning subject matter, self-awareness, and interpersonal dynamics.
  • Quantitative data corroborated the qualitative themes, supporting the perceived value and feasibility.

Conclusions:

  • High-stakes collaborative tests are valuable and feasible in medical education, despite student apprehension.
  • This testing method can serve as both summative and formative evaluation (tests of learning and for learning).
  • Further research is warranted due to the limited existing studies on this assessment methodology.