Path: janda.org/c10 > Syllabus > Topics and Readings > Statistical Inference > Probability Distributions

 Statistical Inference: Probability Distributions
Probability Distributions of Random Variables
•  Two views on probability, compared with my lecture notes
• My "a priori" expectations are based on the expectations without regard to advance empirical observations (Schmidt, p. 217).
• His "relative frequency" view ( p. 214) corresponds to my "empirical" expectations.
• Know these things
• Know the addition rule of probability
• Know the multiplication rule
• Understand the notion of conditional probability for non-exclusive events
• Understand counting simple events
• Distinguish between combinations (in which order of the events or objects is irrelevant) from permutations (for which unique orderings are important).
• Note that there is a formula for determining the number of combinations of n objects taken r at a time.
• Relevance of all this:
• All this stuff applies when, in practice, (1) you are dealing with small categories of events and (2) small numbers of cases.
• Usually, it does not arise in social research, and I know very few researchers who employ these formulas, but the underlying ideas are important to understanding the concept of probability.
• Schmidt's summary on pages 242-243 is quite useful.

Inferential analysis uses the data you collected to report on data you have not collected.
• It treats your cases as a sample drawn to represent some larger population.
• According to rules of inferential analysis, you can infer some facts about the population from your sample.
• Inferential statistics produces estimates of population facts that range between specified intervals with stated degrees of confidence or certainty in your estimates.
• In general, inferential statistics depends on carefully drawn samples of cases:
• The probability of selecting each case must be known.
• The simplest form of such probability sampling is random sampling, in which each case has an equal probability of selection.

Probability distributions for DISCRETE v. CONTINUOUS variables
• Computing the probabilities of outcomes for discrete variables is a complicated matter.
• Knowing how to calculate these probabilities is important when you are dealing with small numbers of cases: e.g.,
• voting patterns on the Supreme Court
• success of clinical treatments for small numbers of patients
• passage of a small number of bills introduced by a small number of congressmen
• In each instance, note the emphasis on small
• One "rule of thumb" for what is "small" is under 30
• When only a small number of cases is involved, the probability of occurrence of each outcome is very sensitive to each case and outcome.
• When larger numbers of cases are involved, computations of probabilities are simplified by using the BINOMIAL THEOREM:
• This states that the probability of r successes,
• given N independent trials with two outcomes (called a Bernoulli experiment),
• is the product of (a) the number of possible sequences that have r successes,
• times (b) the probability of each sequence.
• In symbols, this is represented as:
p(X = r) = nCrprqn-r

• The important point is that as the number of trials (cases) increases, the probability distribution assumes the shape of the normal distribution.
• The normal distribution approximates the binomial distribution, even when N is very small. When N is 40 or larger, the binomial distribution converges on the normal distribution.
• Hence, when the numbers of cases are large, one need not calculate exact probabilities for discrete outcomes.

Earlier in the class, you encountered the normal distribution. If you wish, go here to review that lecture.
Using the Table of Areas under the Normal Curve: The z-score
• You determine the probability of occurrence of a random event in a normal distribution by consulting a table of areas under a normal curve.
• Tables of the normal curve are devised to have a mean of 0 and a standard deviation of 1.
• (e.g., Appendix 1, Table A, distributed with the Schmidt chapter).
• To use any table of the normal curve, you must convert your data to have a mean of 0 and standard deviation of 1.
• This is done by transforming your raw values into z-scores, according to this formula:
z-score =
Using z-scores to read a table of areas under the normal curve

Computational Examples of z-scores:

 Example: percent black in Washington, D.C. in 1980 (Note: not 1990) D.C's Raw score = 70.3 Mean for all states = 10.3 (70.3 - 10.3) = 60 60 / 12.5 = 4.8 standard deviation = 12.5 z-score for D.C. = 4.8
 Example: D.C.'s percent vote for Reagan in 1984 D.C's Raw score = 13 Mean for all states = 60 (13 - 60) = 47 47 / 8.8 = -5.3 standard deviation = 8.8 z-score for D.C. = -5.3
 Comparison with Florida: percent vote for Reagan in 1984 Florida's percent black is 13.8 z-score = (13.8 - 10.3) / 12.5 = .28 Florida's percent for Reagan was 65 z-score = (65 - 60) / 8.8 = .57

Computing z-scores for raw data

• Transforming raw standardizes the data, which makes it easier to compare values in different distributions.
• Limitations of raw data values
• Raw scores of individual cases do not disclose how they vary from the central tendency of the distribution
• One needs to know also the mean of the distribution and its variability to determine if any given score is "far" from the mean
• Properties of the z-score transformation
• It is a linear transformation: does not alter relative positions of observations in the distribution nor change the shape of the original distribution.
• The transformed observations have positive and negative decimal values expressed in standard deviation units.
• The sign of the z-score tells whether the observation is above or below the mean.
• The value of the z-score tells how far above or below it is.
• When transformed into z-scores, all distributions are standardized
• The mean of the transformed distribution is 0.
• The standard deviation of the distribution is 1.
• The variance of the distribution is 1.
• When subjected to a z-score transformation, any set of raw scores that conform to a normal distribution, will conform exactly to the table of areas under the normal curve.
• That is, the likelihood of observing z-scores of certain magnitudes can be read directly from a Table of Areas under the Normal Curve.