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Related Concept Videos

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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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, comparing...
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...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
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.
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...
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...

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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

An especial skill: Support for a learned parameters hypothesis.

Gavin Breslin1, Nicola J Hodges, Rodney Kennedy

  • 1Sport and Exercise Science Research Institute, University of Ulster, Jordanstown Campus, Newtownabbey Co. Antrim, Northern Ireland, UK. g.breslin1@ulster.ac.uk

Acta Psychologica
|January 5, 2010
PubMed
Summary
This summary is machine-generated.

Experts demonstrate an

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Area of Science:

  • Motor learning and control
  • Biomechanics of sports
  • Expertise in sports

Background:

  • The 'especial skill effect' describes enhanced performance in specific contexts for experts.
  • The 'learned parameters' hypothesis suggests experts learn specific adjustments for skilled actions.

Purpose of the Study:

  • To investigate the 'learned parameters' hypothesis as an explanation for the 'especial skill effect' in basketball free-throw shooting.
  • To examine how ball weight influences the 'especial skill effect' and expert performance.

Main Methods:

  • Recorded outcome attainment (shot success) and movement kinematics for 10 expert and 10 novice basketball players.
  • Players performed free-throw shots at various distances (11-19 ft) using regular and heavy basketballs.

Main Results:

  • Experts showed an 'especial skill effect' at the 15 ft free-throw line with a regular basketball, outperforming predictions.
  • This effect disappeared with a heavy basketball, and novices did not exhibit the effect with either ball.
  • Outcome attainment supported the 'learned parameters' hypothesis, but kinematic analysis revealed no distinct motor programs for different distances.

Conclusions:

  • The 'especial skill effect' in basketball free throws appears to be influenced by external factors like ball weight.
  • While performance adjustments suggest learned parameters, the lack of kinematic differentiation indicates skills are not governed by separate motor programs.