Chapter 6: Probability

1. Chapter 6: Probability

2. Probability • Probability is a method for measuring and quantifying the likelihood of obtaining a specific sample from a specific population. • We define probability as a fraction or a proportion. • The probability of any specific outcome is determined by a ratio comparing the frequency of occurrence for that outcome relative to the total number of possible outcomes.

4. Probability (cont.) • Whenever the scores in a population are variable it is impossible to predict with perfect accuracy exactly which score or scores will be obtained when you take a sample from the population. • In this situation, researchers rely on probability to determine the relative likelihood for specific samples. • Thus, although you may not be able to predict exactly which value(s) will be obtained for a sample, it is possible to determine which outcomes have high probability and which have low probability.

5. Probability (cont.) • Probability is determined by a fraction or proportion. • When a population of scores is represented by a frequency distribution, probabilities can be defined by proportions of the distribution. • In graphs, probability can be defined as a proportion of area under the curve.

6. Probability and the Normal Distribution • If a vertical line is drawn through a normal distribution, several things occur. 1. The exact location of the line can be specified by a z-score. 2. The line divides the distribution into two sections. The larger section is called the body and the smaller section is called the tail.

7. Probability and the Normal Distribution (cont.) • The unit normal table lists several different proportions corresponding to each z-score location. • Column A of the table lists z-score values. • For each z-score location, columns B and C list the proportions in the body and tail, respectively. • Finally, column D lists the proportion between the mean and the z-score location. • Because probability is equivalent to proportion, the table values can also be used to determine probabilities.

9. Probability and the Normal Distribution (cont.) • To find the probability corresponding to a particular score (X value), you first transform the score into a z-score, then look up the z-score in the table and read across the row to find the appropriate proportion/probability. • To find the score (X value) corresponding to a particular proportion, you first look up the proportion in the table, read across the row to find the corresponding z-score, and then transform the z-score into an X value.

10. Percentiles and Percentile Ranks • The percentile rank for a specific X value is the percentage of individuals with scores at or below that value. • When a score is referred to by its rank, the score is called a percentile. The percentile rank for a score in a normal distribution is simply the proportion to the left of the score.

12. Probability and the Binomial Distribution • Binomial distributions are formed by a series of observations (for example, 100 coin tosses) for which there are exactly two possible outcomes (heads and tails). • The two outcomes are identified as A and B, with probabilities of p(A) = p and p(B) = q. • The distribution shows the probability for each value of X, where X is the number of occurrences of A in a series of n observations.

13. Probability and the Binomial Distribution (cont.) • When pn and qn are both greater than 10, the binomial distribution is closely approximated by a normal distribution with a mean of μ = pn and a standard deviation of σ = npq. • In this situation, a z-score can be computed for each value of X and the unit normal table can be used to determine probabilities for specific outcomes.

15. Probability and Inferential Statistics • Probability is important because it establishes a link between samples and populations. • For any known population it is possible to determine the probability of obtaining any specific sample. • In later chapters we will use this link as the foundation for inferential statistics.

16. Probability and Inferential Statistics (cont.) • The general goal of inferential statistics is to use the information from a sample to reach a general conclusion (inference) about an unknown population. • Typically a researcher begins with a sample. • If the sample has a high probability of being obtained from a specific population, then the researcher can conclude that the sample is likely to have come from that population. • If the sample has a very low probability of being obtained from a specific population, then it is reasonable for the researcher to conclude that the specific population is probably not the source for the sample.