🔍 What is a Hypothesis?
A hypothesis is a formal statement predicting the outcome of a study. It sets the groundwork for statistical testing and is typically divided into two main types:
1. Null Hypothesis (H₀):
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States that no effect or no difference exists.
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Example: There is no difference in hospital readmission rates between Drug A and Drug B.
2. Alternative Hypothesis (H₁ or Ha):
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States that an effect or difference does exist.
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Example: There is a difference in hospital readmission rates between Drug A and Drug B.
In statistical testing, we start by assuming the null hypothesis is true, and use data to decide whether to reject it in favor of the alternative.
⚠️ Types of Errors in Hypothesis Testing
Even well-designed studies can make mistakes in conclusions. These are known as Type I and Type II errors:
🟥 Type I Error (False Positive):
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Occurs when the null hypothesis is incorrectly rejected.
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You conclude there is a difference/effect when there isn’t.
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Probability of this error is denoted by α (alpha)—commonly set at 0.05.
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Example: Concluding Drug A is better than Drug B, when in fact there is no difference.
🟦 Type II Error (False Negative):
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Occurs when the null hypothesis is incorrectly accepted (not rejected).
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You fail to detect a real effect/difference.
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Probability of this error is denoted by β (beta).
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Example: Concluding there’s no benefit to Drug A when it actually performs better than Drug B.
✅ Power of a Test:
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Power = 1 - β
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It represents the probability of correctly rejecting the null hypothesis (i.e., detecting a true effect).
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Increasing sample size or effect size increases power.
