Use and Abuse of Statistical Inference

This set of flashcards covers the key concepts of errors in statistical inference, including definitions of Type I and Type II errors, significance levels, and the importance of context in hypothesis testing.

What are the two types of errors in statistical inference?

Type I error (False Positive) and Type II error (False Negative).

What does a Type I error indicate in hypothesis testing?

Rejecting the null hypothesis H0 when it is actually true.

What does a Type II error indicate in hypothesis testing?

Accepting the null hypothesis H0 when the alternative hypothesis Ha is true.

In terms of statistical inference, what does the significance level α represent?

The probability of making a Type I error.

In the context of shipments, what does rejecting the shipment signify?

That a Type I error may have occurred if the shipment actually meets the standard.

How does a Type I error affect producers?

It hurts the producer by rejecting good shipments.

How does a Type II error impact consumers?

It hurts the consumer by accepting bad shipments.

Why is it important to consider the context when evaluating errors in statistical testing?

The seriousness of Type I and II errors may differ depending on the context, such as in medical tests.

What are the terms used in the confusion matrix related to hypothesis testing?

True Positive, True Negative, False Positive (Type I Error), False Negative (Type II Error).

What must be prioritized when testing for conditions like diseases or qualifications, like an all-star player?

Focusing on the positive case, as it is usually more meaningful.