A Type I error is committed if we make: ________________________________________________________________________________________________________________________________________________
· a correct decision when the null hypothesis is false
· incorrect decision when the null hypothesis is false
· correct decision when the null hypothesis is true
· incorrect decision when the null hypothesis is true
The Correct Answer and Explanation is:
Correct Answer:
· incorrect decision when the null hypothesis is true
Explanation:
In statistical hypothesis testing, a Type I error occurs when we reject the null hypothesis (H₀) when it is actually true. This is often referred to as a false positive. In simple terms, we believe there is an effect or difference when in fact none exists.
To understand this better, let’s break down the possible outcomes of a hypothesis test:
- Null hypothesis is true and we fail to reject it – Correct decision.
- Null hypothesis is true and we reject it – Type I error.
- Null hypothesis is false and we reject it – Correct decision.
- Null hypothesis is false and we fail to reject it – Type II error.
So, when the null hypothesis is true but we make the decision to reject it, we are committing a Type I error. This means we are mistakenly concluding that there is an effect or relationship when there actually isn’t.
Example:
Suppose a pharmaceutical company is testing a new drug.
- H₀ (null hypothesis): The new drug is no better than the existing treatment.
- H₁ (alternative hypothesis): The new drug is better than the existing treatment.
If researchers reject the null hypothesis and claim that the new drug is effective, but in reality, the new drug offers no improvement, then a Type I error has occurred.
The probability of committing a Type I error is denoted by α (alpha), which is typically set at 0.05. This means there is a 5% risk of rejecting a true null hypothesis.
Understanding Type I errors is critical in research and decision-making because they can lead to false conclusions, wasted resources, or even harmful outcomes if decisions are based on incorrect evidence.
