Related Experiment Video
Updated: Apr 27, 2026

09:49
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
Published on: December 24, 2015
16.2K
Objectivity in confirmation: post hoc monsters and novel predictions.
Studies in History and Philosophy of Science
|July 3, 2014
Summary
This paper develops an objective theory of confirmation by examining predictivism failures. It establishes foundational principles to avoid past issues and revises the confirmation challenge.
Area of Science:
- Philosophy of Science
- Epistemology
- Scientific Methodology
Background:
- Confirmation theory seeks to define the evidential support for hypotheses.
- Predictivism posits that confirming hypotheses requires novel predictions, but faces challenges.
- The problem of "post hoc monsters" highlights limitations in current confirmation accounts.
Purpose of the Study:
- To establish cornerstones for an objective theory of confirmation.
- To learn from the failures of predictivist approaches to confirmation.
- To revise the challenge of defining confirmation beyond inferential-semantical relations.
Main Methods:
- Critical analysis of predictivism, focusing on Worrall's sophisticated version.
- Identification of key principles (cornerstones) for a robust confirmation theory.
- Examination of inferential-semantical relations between hypotheses and evidence.
Main Results:
- Predictivism's failures stem from including contingent considerations.
- A demand to remove contingent factors is identified as a cornerstone.
- The initial challenge regarding confirmation is deemed flawed and requires revision.
Conclusions:
- An objective theory of confirmation necessitates removing contingent factors.
- Lessons from predictivism failures guide the development of a revised confirmation challenge.
- The proposed cornerstones offer a path toward a more complete confirmation account.
Related Concept Videos
Confirmation Biases
6.0K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
6.0K
Hindsight Biases
3.5K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
3.5K
Cause and Effect
10.5K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.5K
Statistical Significance
21.0K
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...
21.0K
Accuracy and Errors in Hypothesis Testing
714
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%...
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%...
714
Correspondence Bias
385
Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
385

