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: "Frequentist Significance Assessment"
Split Justification: This dichotomy separates the two core objectives of frequentist significance assessment: determining the statistical likelihood of observed data given the null hypothesis (typically via p-values) and estimating the size and uncertainty of the observed effect (typically via effect sizes and confidence intervals). These represent distinct but complementary analytical goals within a comprehensive frequentist interpretation.
11
From: "Assessing Evidence Against the Null Hypothesis"
Split Justification: This dichotomy separates the assessment of evidence against the null hypothesis into two core frequentist approaches. One focuses on the likelihood of observing data as extreme as, or more extreme than, the actual data assuming the null hypothesis is true (quantified by the P-value). The other focuses on whether the specific parameter value proposed by the null hypothesis falls within a range of values considered plausible for the true parameter given the observed data (assessed using Confidence Intervals). These are distinct yet complementary ways to evaluate the null.
12
From: "Plausibility of Null Parameter Value"
Split Justification: This dichotomy separates the two primary frequentist statistical methods for evaluating the plausibility of a null parameter value. P-values quantify the probability of observing data as extreme or more extreme than the obtained data under the null hypothesis, thereby indicating how consistent the data is with the null. Confidence intervals define a range of plausible values for the unknown population parameter, allowing direct assessment of whether the null parameter value falls within this consistent range. Together, they offer a comprehensive frequentist perspective on the null's plausibility.
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Topic: "Assessment by Confidence Interval" (W7279)