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Variability: Analysis01:11

Variability: Analysis

143
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
143
What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Fixed Action Patterns01:06

Fixed Action Patterns

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A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Observational Learning01:12

Observational Learning

181
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
181
Cognitive Learning01:21

Cognitive Learning

243
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Related Experiment Video

Updated: Jul 9, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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High variability in learning materials benefits children's pattern practice.

Tongyao Zhang1, Emily R Fyfe1

  • 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.

Journal of Experimental Child Psychology
|December 9, 2023
PubMed
Summary
This summary is machine-generated.

Using varied concrete images aids young children

Keywords:
AbstractionCognitive load theoryOnline studyRepeating patternVariability

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Area of Science:

  • Cognitive Development
  • Educational Psychology

Background:

  • Concrete materials can aid learning, but effectiveness varies.
  • Variability in materials might be a key factor influencing learning outcomes.

Purpose of the Study:

  • To investigate how variability in concrete images affects children's learning of repeating patterns.
  • To determine the optimal level of material variability for pattern acquisition in young children.

Main Methods:

  • 87 children (4–6 years) participated in a pattern learning experiment via Zoom.
  • Children were assigned to Low, Medium, or High Variability training conditions.
  • Performance was assessed during training and on a posttest.

Main Results:

  • Initially, Low Variability training led to better performance.
  • Performance trends reversed by the end of training.
  • High Variability training resulted in the best performance on the posttest.

Conclusions:

  • Variable concrete materials enhance children's ability to learn repeating patterns.
  • High variability may help children generalize and extract underlying pattern structures.
  • Educational interventions should consider material variability for improved learning.