Probability Flashcards
The total possible outcomes in calculating a probability
Sample Space
A standardized score in which the mean of a data set is subtracted from a number and the difference is divided by the standard deviation The calculation tells one how far a number is above or below the mean in terms of standard deviations. Mean and SD of z scores are 0 and 1.
Z score
Some specified occurrence for calculating a probability
Event
In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution, defined on the entire real line. It has a bell-shaped probability density function.
Normal distribution
A discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time, space, distance, area or volume if these events occur independently with a known average rate.
Poisson Distribution
A z score of a normally distributed variable in a population
Standard Normal Deviate
When two events, A and B, are mutually exclusive, the probability that A or B will occur is the sum of the probability of each event such that P(A or B) = P(A) + P(B)
Addition Rule
When two events, A and B, are mutually exclusive, the probability that A AND B will occur is the product of the probability of each even such that P(A and B) = P(A) x P(B)
Multiplication Rule
The probability of testing positive given that a subject has some condition
Sensitivity
The probability of testing negative given that a subject does not have some condition
Specificity
In probability theory, the average of the results obtained from a large number of trials will converge on the expected value, and will tend to become closer as more trials are performed.
Law of Large Numbers
Probability before certain evidence is taken into account. e.g. probability that someone has a disease (without testing them)
Prior Probability
Conditional probability of an event occurring given relevant evidence. e.g. probability of someone having HIV given a positive test.
Posterior Probability
A normal distribution with a mean of 0 and a standard deviation of 1
Standard Normal Curve
p(a) x p(b|a) = p(b) x p(a|b)
Bayes theorem (rule)