Related Experiment Video
Updated: Sep 10, 2025
12:18
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
7.6K
Updating the Homeopathic Algorithms: Handling Confirmation Bias.
Lex Rutten1, Rainer Schäferkordt2, José E Eizayaga3
1Independent Researcher, Breda, The Netherlands.
Homeopathy : the Journal of the Faculty of Homeopathy
|August 27, 2025
Summary
Homeopathic repertorisation algorithms can be improved by addressing confirmation bias using statistical tools. Mathematical corrections, combined with expert knowledge, help refine the accuracy of homeopathic symptom analysis.
Area of Science:
- Homeopathic medicine
- Medical informatics
- Statistical analysis
Background:
- Homeopathy utilizes algorithms for diagnosis, but repertory data is prone to bias.
- Modernizing homeopathic repertorisation requires statistical tools and bias correction.
Purpose of the Study:
- To analyze patterns in likelihood ratios (LRs) in homeopathic case data.
- To investigate methods for correcting confirmation bias in homeopathic repertorisation algorithms.
Main Methods:
- Systematic collection of 731 'Best Chronic Homeopathic Cases' (BCHC).
- Analysis of frequency distributions of likelihood ratios (LRs) using statistical tools.
- Application of mathematical transformations and expert knowledge for bias correction.
Main Results:
- Frequency distributions of LRs exhibited a loglinear progression in the middle with increasing slopes at the ends.
- Confirmation bias in the middle LRs was mathematically correctable using exponentiation.
- An LR of 7 was identified as a suitable maximum for most symptoms, with no significant difference between BCHC and historical data.
Conclusions:
- Confirmation bias in homeopathic repertorisation can be partly corrected using a combination of theory, expert knowledge, and mathematics.
- The study found a notable similarity in confirmation bias patterns between contemporary and historical homeopathic data.
Related Concept Videos
02:07Blind Procedures
12.1K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
12.1K
01:10Homeostatic Imbalance
26.6K
Homeostasis is the maintenance of a stable internal environment within the body, which is crucial for the proper functioning of cells, tissues, organs, and organ systems. The body has various control mechanisms that work together to regulate various physiological parameters such as temperature, blood pressure, pH balance, and fluid balance, to name a few. These control mechanisms are based on feedback loops that can be either positive or negative.
However, sometimes these feedback loops fail,...
However, sometimes these feedback loops fail,...
26.6K
01:31Confirmation Biases
7.2K
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?
7.2K
01:08The Availability Heuristic
6.4K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.4K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
01:52Regression Toward the Mean
6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K