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Dinov, Ivo D.; Palanimalai, Selvam; Khare, Ashwini; Christou, Nicolas – Teaching Statistics: An International Journal for Teachers, 2018
Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends…
Descriptors: Statistical Inference, Sampling, Simulation, Computer Oriented Programs
Hsu, Anne S.; Horng, Andy; Griffiths, Thomas L.; Chater, Nick – Cognitive Science, 2017
Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event…
Descriptors: Statistical Inference, Bayesian Statistics, Evidence, Prediction
Skewes, Joshua C.; Gebauer, Line – Journal of Autism and Developmental Disorders, 2016
Convergent research suggests that people with ASD have difficulties localizing sounds in space. These difficulties have implications for communication, the development of social behavior, and quality of life. Recently, a theory has emerged which treats perceptual symptoms in ASD as the product of impairments in implicit Bayesian inference; as…
Descriptors: Autism, Pervasive Developmental Disorders, Auditory Perception, Bayesian Statistics
Kazak, Sibel; Pratt, Dave – Statistics Education Research Journal, 2017
This study considers probability models as tools for both making informal statistical inferences and building stronger conceptual connections between data and chance topics in teaching statistics. In this paper, we aim to explore pre-service mathematics teachers' use of probability models for a chance game, where the sum of two dice matters in…
Descriptors: Preservice Teachers, Probability, Mathematical Models, Statistical Inference
Swire, Briony; Ecker, Ullrich K. H.; Lewandowsky, Stephan – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
People frequently continue to use inaccurate information in their reasoning even after a credible retraction has been presented. This phenomenon is often referred to as the continued influence effect of misinformation. The repetition of the original misconception within a retraction could contribute to this phenomenon, as it could inadvertently…
Descriptors: Information Utilization, Familiarity, Error Correction, Misconceptions
Rosmaniar, Widhyanti; Marzuki, Shahril Charil bin Hj. – Higher Education Studies, 2016
The purpose of this study is to look closely at how aspects of instructional leadership, and organizational learning affect the quality of madrasah in improving the quality of graduate the state madrasah aliyah. The experiment was conducted using a quantitative approach with descriptive and inferential methods, in inferential methods used…
Descriptors: Principals, Instructional Leadership, Workplace Learning, Organizational Development
Buchanan, Taylor L.; Lohse, Keith R. – Measurement in Physical Education and Exercise Science, 2016
We surveyed researchers in the health and exercise sciences to explore different areas and magnitudes of bias in researchers' decision making. Participants were presented with scenarios (testing a central hypothesis with p = 0.06 or p = 0.04) in a random order and surveyed about what they would do in each scenario. Participants showed significant…
Descriptors: Researchers, Attitudes, Statistical Significance, Bias
Austerweil, Joseph L.; Griffiths, Thomas L. – Cognitive Psychology, 2011
Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of…
Descriptors: Visual Stimuli, Novelty (Stimulus Dimension), Bayesian Statistics, Learning
Noll, Jennifer; Shaughnessy, J. Michael – Journal for Research in Mathematics Education, 2012
Sampling tasks and sampling distributions provide a fertile realm for investigating students' conceptions of variability. A project-designed teaching episode on samples and sampling distributions was team-taught in 6 research classrooms (2 middle school and 4 high school) by the investigators and regular classroom mathematics teachers. Data…
Descriptors: Sampling, Mathematics Teachers, Middle Schools, High Schools
Back, Elisa; Apperly, Ian A. – Cognition, 2010
A recent study by Apperly et al. (2006) found evidence that adults do not automatically infer false beliefs while watching videos that afford such inferences. This method was extended to examine true beliefs, which are sometimes thought to be ascribed by "default" (e.g., Leslie & Thaiss, 1992). Sequences of pictures were presented in which the…
Descriptors: Reaction Time, Personality, Inferences, Cognitive Development
Sobel, David M.; Munro, Sarah E. – Developmental Psychology, 2009
In 5 experiments the authors examined children's understanding of causal mechanisms and their reasoning about base rates across domains of knowledge. Experiment 1 showed that 3-year-olds interpret objects activating a machine differently from a novel agent liking each object; children are more likely to treat the latter as indicating the objects…
Descriptors: Statistical Inference, Inferences, Influences, Young Children
Maraun, Michael; Gabriel, Stephanie – Psychological Methods, 2010
In his article, "An Alternative to Null-Hypothesis Significance Tests," Killeen (2005) urged the discipline to abandon the practice of "p[subscript obs]"-based null hypothesis testing and to quantify the signal-to-noise characteristics of experimental outcomes with replication probabilities. He described the coefficient that he…
Descriptors: Hypothesis Testing, Statistical Inference, Probability, Statistical Significance
Leigland, Sam – Behavior Analyst, 2010
The experimental analysis of behavior began as an inductively oriented, empirically based scientific field. As the field grew, its distinctive system of science--radical behaviorism--grew with it. The continuing growth of the empirical base of the field has been accompanied by the growth of the literature on radical behaviorism and its…
Descriptors: Behaviorism, Behavioral Science Research, Research, Scientific Concepts
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Psychological Review, 2009
Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…
Descriptors: Causal Models, Prior Learning, Logical Thinking, Statistical Inference
Turk-Browne, Nicholas B.; Scholl, Brian J. – Journal of Experimental Psychology: Human Perception and Performance, 2009
The environment contains considerable information that is distributed across space and time, and the visual system is remarkably sensitive to such information via the operation of visual statistical learning (VSL). Previous VSL studies have focused on establishing what kinds of statistical relationships can be learned but have not fully explored…
Descriptors: Visual Discrimination, Undergraduate Students, Experiments, Time Perspective
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