Research is a lot like Painting a Room in a House…Funny Story Don’t believe me continue reading. This is an example of Pain, Suffering, and Sweet Relief. Yes! Newsflash, this is not like taxes, you are not exempt! At the very least you can expect to go through relatively similar phases. WE ALL HAVE! Phase […]
A Research Allegory… Painting: Dissertation 7 Phases of Grief Read More »
Dark Side of Statistics An example of an unanswerable question ChatGPT is unable to solve. Response by ChatGPT: Why Should This Treatment Not Be Recommended? Conclusion Even though the p-value is extremely small (p = 0.0001), the tiny effect size (d = 0.01) suggests that the treatment provides no meaningful advantage over a placebo or
ChatGPT AI, You’re wrong! Developing Unanswerable Questions Read More »
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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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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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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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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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)
Type I vs Type II Errors: A Researcher’s Guide Read More »
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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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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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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