Chapter 7 - Probability And The Normal Curve Flashcards
Mathematical probability
Type of probability that is based upon equally likely outcomes that can be calculated such as coin tosses or dice rolls
Empirical probability
Type of probability that is based upon observed data or research from the last such as estimating probabilities based upon survey results from a random sample of respondents.
Subjective probability
Individual opinion about the probability of an event
Law of probability
The probability of an event is the number of ways it happen, as a fraction of the number of all possible things that could happen. This rule holds if all possible outcomes of an vent are equally likely (I.e. flipping a coin). The probability that either event will occur is equal to the ratio of “success” to the number of possible outcomes.
Gambler’s Fallacy
The probability that you could flip a coin and it would come up “heads” is one out of two or 50%. Note also that the probability of flipping a head extends to the next toss and every toss thereafter (always stays 50%).
Mutually exclusive
The occurrence of one vent prevents the occurrence of another. They cannot occur simultaneously. (Example: trial outcome: acquitted or guilty)
Addition rule
Used to calculate the probability of mutually exclusive events. If two events are mutually exclusive, the probability of their occurrence is equal to the sum of their separate probabilities.
Multiplication rule
Covers events that are not muruay exclusive, such that the probability of their occurrence is equal to the product of their separate probabilities
Bernoulli process (binomial distribution)
The outcome can be classified into one of two mutually exclusive categories. There are three aspects: (1) the researcher usually designated one outcome as a “success” (it’s probability = p) and the other as a “failure” (it’s probability = q). (2) the probability of success is unchanged from one trial to another. (3) the outcomes are independent.
Central limit theorem
If repeated random samples of a given size are drawn from any population (with a mean of u and a variance of I), then as the sample size becomes large the sampling distribution of sample means approaches normality
Sampling distribution of the mean
The distribution of the means of all possible samples drawn from a population. Under the central limit theorem, the sample mean is normally distributed for large values of N.
Standard error of the mean
The standard deviation of the sampling distribution of the mean. It indicates how far a sample mean falls from the population mean, a difference known as the “error”.
Confidence interval
An estimate of the population value based upon sample results.
