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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Related Experiment Video

Updated: Dec 3, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
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Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

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Soft pre-rough sets and its applications in decision making.

M El Sayed1,2, Abdul Gawad A Q Al Qubati1, M K El-Bably2

  • 1Department of Mathematics, College of Science and Arts, Najran University, Kingdom of Saudi Arabia.

Mathematical Biosciences and Engineering : MBE
|October 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, generalized soft rough set model for handling uncertain data. The new approach is applied to medical decision-making, specifically for COVID-19 diagnosis, demonstrating its practical applicability.

Keywords:
decision makingsoft pre-rough setssoft rough setssoft sets

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Area of Science:

  • * Mathematics
  • * Computer Science
  • * Data Science

Background:

  • * Soft set theory and rough set theory are distinct mathematical frameworks for dealing with uncertainty.
  • * Existing models may not fully capture the complexities of real-world uncertain data.
  • * There is a need for integrated approaches that leverage the strengths of both soft and rough set theories.

Purpose of the Study:

  • * To introduce a novel modification and generalization of the soft rough set model.
  • * To explore and establish the fundamental properties of the proposed generalized soft rough sets.
  • * To apply the new model for mathematical modeling of uncertain data and ambiguity resolution.

Main Methods:

  • * Development of a new generalized soft rough set framework.
  • * Theoretical analysis of the basic properties of the proposed approaches.
  • * Application of the model for decision-making in uncertain environments.

Main Results:

  • * A new, generalized soft rough set model is successfully developed.
  • * The basic properties of the proposed model are thoroughly investigated.
  • * The model demonstrates effectiveness in handling ambiguous and uncertain data.

Conclusions:

  • * The proposed generalized soft rough set model offers a robust framework for uncertain data analysis.
  • * The model's utility is validated through a medical application in COVID-19 diagnosis decision-making.
  • * An algorithm based on the new concepts enhances decision-making processes.