PSY 231-Statistics Tutorial
Review of Statistical Concepts
(1) descriptive statistics: allow you to summarize (or describe) the data; includes measures of central
tendency (mean, median, mode), measures of variability (range, standard deviation, variance), graphs, and
tables.
(2) inferential statistics: allow you to test hypotheses; use sample statistics to learn about the population,
while accounting for sampling error; look for evidence against the null hypothesis
(3) sampling error: Whenever you sample from a population (which is done in most psychological
studies), you are only choosing a small subset of that population. Therefore, it is very unlikely that the
statistics you calculate for the sample will match the values you would have calculated for the whole
population (called parameters).The difference between the population parameters (if they'd been
calculated) and the sample statistics (that you did calculate) is sample error. Sampling error is
present in most studies because we cannot test the entire population. We must rely on sample statistics to
learn about the population we are interested in.
(4) alpha: decision criterion value set by the researcher; highest probability value you'll accept as
evidence against the null hypothesis; chance of a Type I error
(5) Type I error: probability that the null hypothesis is false when you've found evidence against it using
hypothesis testing (i.e., you rejected the null hypothesis as your decision)
(6) Type II error: probability that the null hypothesis is true when you've found no evidence against it
using hypothesis testing (i.e., you failed to reject the null hypothesis as you decision)
(7) power: probability that you correctly reject the null hypothesis (i.e., the null hypothesis is false and
you find evidence against it using hypothesis testing); likelihood that your inferential statistics test will
find an effect when it exists