conditional probability in problems with sampling

Question 1

1. What is the difference between false positive and false negative error?

Answer 1

A “false positive” is when a good quality item gets rejected, and a “false negative” is when a poor-quality item gets accepted.

Question 2

2. What is the difference between sensitivity, specificity and P-value?

Answer 2

The sensitivity of a test (also called the true positive rate) is defined as the proportion of people with the disease who will have a positive result. The specificity of a test (also called the True Negative Rate) is the proportion of people without the disease who will have a negative result. The positive predictive value (PPV) is the probability that a positive result in a hypothesis test means that there is a real effect.

Question 3

3. What is base rate fallacy?

Answer 3

False positive tests are more probable than true positive tests, occurring when the overall population has a low incidence of a condition and the incidence rate is lower than the false positive rate.

Question 4

4. What is conditional probability?

Answer 4

Conditional probability is a measure of the probability of an event occurring given that another event has (by assumption, presumption, assertion or evidence) occurred.

Question 5

6. How can statistics mislead?

Answer 5

1. Statistical significance doesn’t imply practical significance.
2. Irrelevant Plots
3. Correlation doesn’t imply causation:
4. Simpson’s paradox: Simpson’s paradox, or the Yule–Simpson effect, is a phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined.
5. Sampling: Data collected needs to be the right amount, statistics with small sample size is usually less accurate.