Flashcards in Chapter 9 Deck (33)
Statistical procedures designed to determine if differences and relationships found in sample data are sufficiently large that they can be assumed to be true at the population level
Degree of Uncertainty
All inferences from samples to populations involve a degree of uncertainty. The amount of uncertainty is determined by a number of factors, including sampling error and measurement error.
This is differences in estimates of a population parameter based upon different testings of the same sample using the same instrument. These differences are the result of unreliability in our measurement instrument.
This is the differences in estimates of a population parameter based upon different samples drawn from the same population. If the samples were randomly drawn and all of the same size, the amount of error (at a certain probability level) can be calculated.
There is no difference between the groups (or the difference is in the unpredicted direction)
There is a difference between the groups (or the difference is in the predicted direction)
level of significance
also known as a level
If the _____________ would have generated the sample statistic less often by chance than the a level, the researcher rejects the ______ and affirms the alternative.
null hypothesis, null
Type I Error
Rejecting the null when it is in fact true
Type II Error
Failing to reject the null when it is in fact false
Difference between group means
larger differences make it easier to achieve significance, all other things being equal
Sample size (N)
larger sample sizes reduce sampling error and make it easier to achieve significance, all other things being equal
Reliability of the instruments
more reliable instruments reduce measurement error, and make it easier to achieve significance, all other things being equal
An interval within which we believe the true population parameter falls, at a given level of confidence (usually 95% or 99%)
Statistical significance answers the question, “Are the results likely to be due to sampling error?”
Sample size does not play a role in ______________.
Cohen’s d =
Mean One – Mean Two/Standard Deviation (Either Pooled or for Control)
Coefficient of Determination =
r squared (for correlations)
Eta Squared =
Sum of Squared Deviations Between/Sum of Squared Deviations Total
Inferential statistical tests that make a number of assumptions about the data (normality, equality of group variances, and interval/ratio level of measurement)
Inferential statistical tests that make relatively few assumption about the data
are more “powerful” than non-parametric statistics; that is, they are more likely to reject the null hypothesis when it is in fact false
Tests for differences between the means of two groups.
Independent samples t-test
Used to test differences in means of two different groups of subjects
Paired dependent samples t-test
Used to test differences in means of two groups of subjects who are either the same or matched on one or more variables
T-test for correlations
Tests whether a sample correlation is large enough to indicate a relationship different from zero in the population
Simple Analysis of Variance (One-Way ANOVA)
Tests for differences in the means of three or more groups representing levels of a single IV factor
Factorial Analysis of Variance (N-way ANOVA)
Allows testing of multiple IVs, each of which may have many levels.
Allows determination of the significance of each IV
Allows determination of the significance of changes in an IV as a function of changes in other IVs
techniques have a single DV