Q.15 Following are few statements with reference to Biostatistics.
Choose the statement/s which are incorrect.
- (A) Type II error : α – error
- (B) Type I error : β – error
- (C) Correct null hypothesis is rejected in β – error
- (D) Wrong null hypothesis is accepted in α – error
Choose the correct option from the options given below:
- (A) and (B) only.
- (A), (B) and (C) only.
- (A), (B), (C) and (D).
- (B), (C) and (D) only.
Type I and Type II errors are fundamental concepts in biostatistics hypothesis testing, and all given statements misrepresent them.
Correct Definitions
Type I error (α-error) occurs when a true null hypothesis (H₀) is incorrectly rejected, like a false positive. Type II error (β-error) happens when a false null hypothesis is not rejected (accepted), like a false negative.
Option Analysis
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(A) Type II error: α-error
Incorrect. Type II error is β-error, not α-error. -
(B) Type I error: β-error
Incorrect. Type I error is α-error, not β-error. -
(C) Correct null hypothesis is rejected in β-error
Incorrect. Rejecting a correct H₀ defines Type I (α) error, not β-error. -
(D) Wrong null hypothesis is accepted in α-error
Incorrect. Accepting a wrong H₀ (false H₀) defines Type II (β) error, not α-error.
Answer: (A), (B), (C) and (D).
Introduction to Type I Error and Type II Error in Biostatistics
In biostatistics hypothesis testing, Type I error (α-error) and Type II error (β-error) represent critical mistakes that affect research validity, especially for GATE Life Sciences aspirants. These errors determine if you wrongly reject a true null hypothesis or fail to detect a real effect. Mastering Type I error Type II error biostatistics ensures accurate statistical decisions in molecular biology experiments and clinical trials.
What is Type I Error (α-Error)?
Type I error occurs when you reject the null hypothesis (H₀) despite it being true—essentially a false positive.
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Probability: Denoted by α (e.g., 0.05 significance level).
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Example: Declaring a drug ineffective when it matches placebo (true H₀: no difference).
Reducing α lowers Type I error risk but increases Type II error chance.
What is Type II Error (β-Error)?
Type II error is failing to reject a false null hypothesis—a false negative.
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Probability: Denoted by β; power of test = 1 – β.
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Example: Concluding no difference between treatments when one truly works better.
Factors like small sample size raise β-error risk.
Error Type Description When It Happens Probability Symbol Consequence Type I (α-error) Reject true H₀ Correct null hypothesis rejected α False positive Type II (β-error) Accept false H₀ Wrong null hypothesis accepted β False negative Common Misconceptions in Biostatistics Exams
GATE questions often test confusions like:
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“Correct null hypothesis rejected = β-error” (Wrong: It’s α-error).
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“Wrong null hypothesis accepted = α-error” (Wrong: It’s β-error).
Avoid mixing α with Type II error or β with Type I error for scoring high.
Tips for GATE Life Sciences Preparation
Practice PYQs on Type I error Type II error biostatistics to differentiate errors quickly. Use power analysis (1-β) for study designs in genetics/microbiology research. Focus on α = 0.05/0.01 levels common in exams.
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