1
From: "Human Potential & Development."
Split Justification: Development fundamentally involves both our inner landscape (**Internal World**) and our interaction with everything outside us (**External World**). (Ref: Subject-Object Distinction)..
2
From: "Internal World (The Self)"
Split Justification: The Internal World involves both mental processes (**Cognitive Sphere**) and physical experiences (**Somatic Sphere**). (Ref: Mind-Body Distinction)
3
From: "Cognitive Sphere"
Split Justification: Cognition operates via deliberate, logical steps (**Analytical Processing**) and faster, intuitive pattern-matching (**Intuitive/Associative Processing**). (Ref: Dual Process Theory)
4
From: "Analytical Processing"
Split Justification: Analytical thought engages distinct symbolic systems: abstract logic and mathematics (**Quantitative/Logical Reasoning**) versus structured language (**Linguistic/Verbal Reasoning**).
5
From: "Quantitative/Logical Reasoning"
Split Justification: Logical reasoning can be strictly formal following rules of inference (**Deductive Proof**) or drawing general conclusions from specific examples (**Inductive Reasoning Case Study**). (L5 Split)
6
From: "Inductive Reasoning Case Study"
Split Justification: Induction involves forming general rules (**Hypothesis Generation**) and testing their predictive power (**Hypothesis Testing**). (L6 Split)
7
From: "Hypothesis Testing"
Split Justification: Testing a hypothesis involves designing a fair test (**Designing a Simple Experiment**) and drawing conclusions from the outcome (**Interpreting Results**).
8
From: "Interpreting Results"
Split Justification: This dichotomy separates the objective, quantitative evaluation of the evidence from the hypothesis (Statistical Significance Assessment) from the more qualitative, integrative process of understanding the real-world implications, limitations, and broader significance of those findings (Contextual Meaning Derivation).
9
From: "Statistical Significance Assessment"
Split Justification: This dichotomy represents the two primary and distinct statistical paradigms used for conceptualizing, quantifying, and interpreting statistical significance, each employing unique methodological frameworks and metrics (e.g., p-values vs. Bayes factors).
10
From: "Bayesian Significance Assessment"
Split Justification: This dichotomy differentiates between Bayesian approaches that assess significance by directly comparing the posterior probability or evidence for competing hypotheses (e.g., using Bayes Factors) and those that assess significance by examining the credibility of specific parameter values within their posterior distribution (e.g., using credible intervals or Regions of Practical Equivalence, ROPEs).
11
From: "Bayesian Parameter Credibility for Significance"
Split Justification: This split differentiates between two primary methods of using Bayesian parameter credibility for significance. One focuses on directly calculating the posterior probability of a parameter falling into specific regions of interest (e.g., P(θ > 0 | data)), while the other focuses on constructing credible intervals and interpreting their relationship to null or reference values (e.g., whether a 95% credible interval excludes zero).
12
From: "Credible Interval-Based Significance Assessment"
Split Justification: This dichotomy represents the two fundamental outcomes when using a credible interval to assess a null hypothesis: either the interval excludes the null value, leading to a conclusion of significance, or it includes the null value, leading to a conclusion of non-significance (or insufficient evidence for a difference). These are mutually exclusive conditions that comprehensively cover how credible intervals are used for significance assessment against a null hypothesis.
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Topic: "Significance Assessment by Null Inclusion" (W8047)