chap 13 key takeaways Flashcards
null hypothesis testing is a formal approach to deciding whether a:
statistical relationship in a sample reflects a real relationship in the population or is just due to chance
the logic of null hypothesis testing involves:
assuming that the null hypothesis is true, finding how likely the sample result would be if this assumption were correct, then making a decision
if the sample result would be unlikely if the null hypothesis were true, then
it is rejected in favor of the alternative hypothesis. and if not unlikely, then null hypothesis would be retained
the p value is:
the probability of obtaining the sample result if the null hypothesis were true
the p value is based on these two considerations:
relationship strength and sample size
true or false: statistical significance is the same as relationship strength or importance:
false
the most common null hypothesis test:
the t test
the one-sample t test is used for:
comparing one sample mean with a hypothetical population mean of interest
the dependent samples t test is used to:
compare two means in a within-subjects design
the independent samples t test is used to:
compare two means in a between-subjects design
to compare more than two means, the most common null hypothesis test is the
analysis of variance (ANOVA)
the one-way ANOVA is used for:
between subjects designs with one independent variable
the repeated measures ANOVA is used for:
within-subjects designs
the factorial ANOVA is used for:
factorial designs
A null hypothesis test of Pearson’s r is used to
compare a sample value of Pearson’s r with a hypothetical population value of 0
true or false: The decision to reject or retain the null hypothesis is not guaranteed to be correct.
true
a type 1 error occurs when:
one rejects the null hypothesis when it is true
a type 2 error occurs when:
one fails to reject the null hypothesis when it is false
The statistical power of a research design is the
probability of rejecting the null hypothesis given the expected strength of the relationship of the population and sample size
Null hypothesis testing has been criticized on the grounds that
researchers misunderstand it, that it is illogical, and that it is uninformative.
In recent years psychology has grappled with a failure to
replicate research findings
One response to this “replicability crisis” has been the
emergence of open science practices, which increase the transparency and openness of the research process
