Understanding Hypothesis Testing in Research

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Research Fundamentals #1 The Jerzy Neyman – Egon Pearson Model is the most widely used approach in research testing two competing hypotheses Common Reporting Mistake & CorrectionNovice Mistake to Avoid:— “The Alternative (Researcher’s) Hypothesis (H₁) was accepted.”Corrected Reporting Style:+ “The Null Hypothesis (H₀) was rejected.”Why?• More Neutral & Professional: Saying “we rejected H₀” rather than […]

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Understanding Statistical Power and Sample Size in Research

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Research Fundamentals #5 Learning ApproachThis lesson builds upon our understanding of Alpha (α) and Beta (β) Levels. If you haven’t reviewed those concepts yet, go back and do so first—each step in this series follows a structured learning approach. The Importance of Sample Size in Research Factors That Influence Sample Size Key Points Power Analysis:

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Understanding Effect Size in Research

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Research Fundamentals #7 Effect Size Qualities Small effect: A subtle but potentially meaningful difference.Medium effect: A noticeable impact.Large effect: A strong and substantial difference. Conventional Measures of Effect Size Pearson’s r (for correlation strength):Small: 0.1Medium: 0.3Large: 0.5 Cohen’s d (for comparing two means):Small: 0.2Medium: 0.5Large: 0.8 Eta-squared (η²) (for ANOVA effect size):Small: 0.01Medium: 0.06Large: 0.14

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Statistical vs. Practical Significance Explained

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Research Fundamentals #8 Key Features Interpretation: A statistically significant result suggests strong evidence against the null hypothesis.Limitations: Statistical significance does not indicate the strength or importance of an effect. Example: A new medication reduces symptoms with p = 0.03, but the actual improvement is minimal. Understanding Practical Significance Key Features Interpretation: A result can be

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Understanding Confidence Intervals and MOE in Statistics

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Research Fundamentals #6 This lesson builds upon our understanding of Sample Size and Power Analysis. If you haven’t reviewed those concepts yet, go back and do so first—each step in this series follows a structured learning approach. What Are Confidence Intervals? Example someone scores a 104 on an IQ test we may say there true

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Type I vs Type II Errors: A Researcher’s Guide

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Research Fundamentals #4 Type I Error (False Positive): Rejecting a true null hypothesis (H₀). Example: A clinical trial concludes a new antidepressant medication is effective Concluding a drug is when it actually isn’t. Controlled by Alpha (α), the significance level (e.g., 0.01). Type II Error (False Negative): Failing to reject a false null hypothesis (H₀)

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Understanding P-Values and Their Role in Hypothesis Testing

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Research Fundamentals #3 Learning Objectives: What is a P-Value? p-value represents the probability of obtaining results at least as extreme as the observed data, assuming the null hypothesis (H₀) is true. It helps determine whether the observed effect is statistically significant.Low p-value (≤ α): Strong evidence against H₀, leading to its rejection.High p-value (> α):

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Understanding Alpha (α) and Beta (β) Levels in Research

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Research Fundamentals #2 (Everything you need to know) Learning Objectives: What is Alpha (α)? The alpha level (α) (significance level) represents the probability of making a Type I error—rejecting the null hypothesis (H₀) when it is actually true.• Common α values:α = 0.05 (Psychological Research)α = 0.01 (Medical Research)A lower Alpha (α) reduces the risk

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