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Related Concept Videos

Inductive Reasoning00:59

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
Deductive Reasoning01:16

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Reasoning01:30

Reasoning

Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...

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Deduced inference in the analysis of experimental data.

Kevin D Bird1

  • 1University of New South Wales, Sydney, New South Wales 2052, Australia. k.bird@unsw.edu.au

Psychological Methods
|July 20, 2011
PubMed
Summary

This study introduces deduced inference, a statistical method that allows deriving new confidence intervals from existing ones without increasing error rates. This approach offers a practical alternative to post hoc analyses, especially in complex factorial experiments.

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Area of Science:

  • Statistics
  • Experimental Design

Background:

  • Confidence intervals are crucial for statistical inference.
  • Multiple comparisons in statistical analysis can lead to inflated error rates.
  • Post hoc analyses are common but can be complex and error-prone.

Purpose of the Study:

  • To introduce and explain the concept of deduced inference for confidence intervals.
  • To demonstrate that deduced inference does not inflate experimentwise error rates.
  • To present deduced inference as a useful alternative to traditional post hoc analyses, particularly in factorial experiments.

Main Methods:

  • Utilizing existing confidence intervals for linearly independent contrasts to deduce interval inferences for other contrasts.
  • Applying the deduced inference method to analyze factorial experiments.
  • Comparing the precision of deduced inference with direct inference.

Main Results:

  • Deduced inference allows for the derivation of interval inferences on all contrasts from a set of J-1 linearly independent contrasts.
  • Deduced inference does not inflate the experimentwise error rate.
  • While less precise than direct inference, deduced inference is a viable alternative, especially for avoiding issues in factorial experiment analyses.

Conclusions:

  • Deduced inference provides a statistically sound method for expanding inferences from a set of contrasts.
  • This method offers a valuable tool for researchers, simplifying complex analyses and controlling error rates.
  • The application of deduced inference in factorial experiments can circumvent common analytical challenges.