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Learning Module

Hypothesis Testing Framework

Learn to make evidence-based decisions from dental research data. Explore every step of hypothesis testing with interactive examples.

H₀ vs H₁ Type I / II Errors P-values Statistical Power
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Test claims
Determine whether a treatment truly works or the result is just chance.

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Dental research
Compare fluoride treatments, evaluate new materials, assess therapies.

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Make decisions
Use p-values and significance levels to draw reliable conclusions.

Core Concepts

Click any card to explore with interactive examples.

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H₀ & H₁
Null vs Alternative
Tap to explore →
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Significance (α)
Threshold level
Tap to explore →
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P-value
Probability of chance
Tap to explore →
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Type I & II Errors
False decisions
Tap to explore →
Statistical Power
Detecting real effects
Tap to explore →
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Test Statistics
t, χ², F values
Tap to explore →
Hypothesis Testing Decision Machine

Choose a dental scenario and watch the full testing process step-by-step.

Live P-value Explorer

Drag the slider and see how p-value interpretation changes in real time.

p = 0.05
α=0.01
α=0.05
α=0.10
0.00 0.25 0.50 0.75 1.00
Borderline significant at α = 0.05. Evidence warrants careful review.
Error Type Interactive Matrix

Click each cell to see dental examples. Understand how decisions can go wrong.

H₀ is TRUE
No real effect
H₀ is FALSE
Real effect exists
Reject H₀
TYPE I ERROR
False Positive (α)
Click for example
CORRECT
True Positive (Power)
Click for example
Fail to reject H₀
CORRECT
True Negative (1-α)
Click for example
⚠️
TYPE II ERROR
False Negative (β)
Click for example
Dental Research Simulation

Run a simulated clinical trial and watch every step of the hypothesis testing process.

Summary Reference Table
Concept Symbol Meaning Common Values
Null Hypothesis H₀ No effect or difference exists -
Alternative Hypothesis H₁ A real effect or difference exists -
Significance Level α Max acceptable probability of Type I error 0.05, 0.01, 0.001
P-value p Probability of data given H₀ is true p < 0.05 = significant
Type I Error α Rejecting a true H₀ (false positive) 5% when α = 0.05
Type II Error β Failing to reject a false H₀ (false negative) 20% when Power = 0.80
Statistical Power 1 - β Probability of detecting a real effect 0.80, 0.90
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Always report
Effect size + p-value + confidence intervals for transparent research.

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Sample size matters
Small samples reduce power. Plan adequate n before your study.

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Not proof
Failing to reject H₀ does not prove it true — only insufficient evidence.

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