PSYCH 1073
CH. 8-13 Key Terms
Chapter 8: Introduction to Hypothesis Testing
➔ Hypothesis testing: A statistical method that uses sample data to evaluate a hypothesis
about a population. 1) State the hypothesis and set alpha level … 2) Locate critical
regions using alpha level… 3) Compute test statistic for sample … 4) Make a decision to
accept or reject the null.
➔ Null hypothesis (H0): States that in the general population there is no change, no
difference, or no relationship. Predicts that the independent variable (treatment) has no
effect on the dependent variable (scores) for the population.
➔ Two-tailed: H0: μ __ = (average, population of interest) and H1: μ __ ≠ (average,
population of interest.
➔ One-tailed: H0: > or < and H1: < or > (Depends on direction)
➔ Alternative hypothesis (H1): States that there is a change, a difference, or a relationship
for the general population. Predicts that the independent variable (treatment) does have
an effect on the dependent variable.
➔ Level of significance OR Alpha level (a): Is a probability value that is used to define the
concept of “very unlikely” in a hypothesis test. The alpha level for a hypothesis test is the
probability that the test will lead to a Type I error. That is, the alpha level determines the
probability of obtaining sample data in the critical region even though the null hypothesis
is true.
➔ Critical region: Composed of the extreme sample values that are very unlikely (as
defined by the alpha level) to be obtained if the null hypothesis is true. The boundaries
for the critical region are determined by the alpha level. If sample data fall in the critical
region, the null hypothesis is rejected.
➔ Test statistic: A statistic that summarizes the sample data in a hypothesis test. Used to
determine whether or not the data are in the critical region.
, ➔ Type I error: A Type I error occurs when a researcher rejects a null hypothesis that is
actually true. In a typical research situation, a Type I error means the researcher
concludes that a treatment does have an effect when in fact it has no effect.
➔ Type II error: A Type II error occurs when a researcher fails to reject a null hypothesis
that is really false. In a typical research situation, a Type II error means that the
hypothesis test has failed to detect a real treatment effect.
➔ Beta: Beta is the probability of a Type II error.
➔ Significant: A result is said to be significant or statistically significant if it is very
unlikely to occur when the null hypothesis is true. That is, the result is sufficient to reject
the null hypothesis. Thus, a treatment has a significant effect if the decision from the
hypothesis test is to reject null.
➔ Directional test/One-tailed test: A directional test is a hypothesis test that includes a
directional prediction in the statement of the hypotheses and places the critical region
entirely in one tail of the distribution. The statistical hypotheses (H0 and H1) specify
either an increase or a decrease in the population mean. That is, they make a statement
about the direction of the effect.
➔ Non-directional/Two-tailed: Establishes a significant change, but does not specify.
➔ Effect size: A measure of effect size is intended to provide a measurement of the absolute
magnitude of a treatment effect, independent of the size of the samples being used.
➔ Cohen’s d: A standard measure of effect size computed by dividing the sample mean
difference by the sample standard deviation.
➔ Power: The power of a statistical test is the probability that the test will correctly reject a
false null hypothesis. That is, power is the probability that the test will identify a
treatment effect if one really exists.
➔ R²: % of variability of scores. Variability accounted for divided by total variability.
Chapter 9: Introduction to the t Statistic
➔ Estimated standard error (SM): Is used as an estimate of the real standard error (𝛔M)
when the value of (𝛔) is unknown. It is computed from the sample variance or sample
CH. 8-13 Key Terms
Chapter 8: Introduction to Hypothesis Testing
➔ Hypothesis testing: A statistical method that uses sample data to evaluate a hypothesis
about a population. 1) State the hypothesis and set alpha level … 2) Locate critical
regions using alpha level… 3) Compute test statistic for sample … 4) Make a decision to
accept or reject the null.
➔ Null hypothesis (H0): States that in the general population there is no change, no
difference, or no relationship. Predicts that the independent variable (treatment) has no
effect on the dependent variable (scores) for the population.
➔ Two-tailed: H0: μ __ = (average, population of interest) and H1: μ __ ≠ (average,
population of interest.
➔ One-tailed: H0: > or < and H1: < or > (Depends on direction)
➔ Alternative hypothesis (H1): States that there is a change, a difference, or a relationship
for the general population. Predicts that the independent variable (treatment) does have
an effect on the dependent variable.
➔ Level of significance OR Alpha level (a): Is a probability value that is used to define the
concept of “very unlikely” in a hypothesis test. The alpha level for a hypothesis test is the
probability that the test will lead to a Type I error. That is, the alpha level determines the
probability of obtaining sample data in the critical region even though the null hypothesis
is true.
➔ Critical region: Composed of the extreme sample values that are very unlikely (as
defined by the alpha level) to be obtained if the null hypothesis is true. The boundaries
for the critical region are determined by the alpha level. If sample data fall in the critical
region, the null hypothesis is rejected.
➔ Test statistic: A statistic that summarizes the sample data in a hypothesis test. Used to
determine whether or not the data are in the critical region.
, ➔ Type I error: A Type I error occurs when a researcher rejects a null hypothesis that is
actually true. In a typical research situation, a Type I error means the researcher
concludes that a treatment does have an effect when in fact it has no effect.
➔ Type II error: A Type II error occurs when a researcher fails to reject a null hypothesis
that is really false. In a typical research situation, a Type II error means that the
hypothesis test has failed to detect a real treatment effect.
➔ Beta: Beta is the probability of a Type II error.
➔ Significant: A result is said to be significant or statistically significant if it is very
unlikely to occur when the null hypothesis is true. That is, the result is sufficient to reject
the null hypothesis. Thus, a treatment has a significant effect if the decision from the
hypothesis test is to reject null.
➔ Directional test/One-tailed test: A directional test is a hypothesis test that includes a
directional prediction in the statement of the hypotheses and places the critical region
entirely in one tail of the distribution. The statistical hypotheses (H0 and H1) specify
either an increase or a decrease in the population mean. That is, they make a statement
about the direction of the effect.
➔ Non-directional/Two-tailed: Establishes a significant change, but does not specify.
➔ Effect size: A measure of effect size is intended to provide a measurement of the absolute
magnitude of a treatment effect, independent of the size of the samples being used.
➔ Cohen’s d: A standard measure of effect size computed by dividing the sample mean
difference by the sample standard deviation.
➔ Power: The power of a statistical test is the probability that the test will correctly reject a
false null hypothesis. That is, power is the probability that the test will identify a
treatment effect if one really exists.
➔ R²: % of variability of scores. Variability accounted for divided by total variability.
Chapter 9: Introduction to the t Statistic
➔ Estimated standard error (SM): Is used as an estimate of the real standard error (𝛔M)
when the value of (𝛔) is unknown. It is computed from the sample variance or sample
