PSY295 Exam 2 Flashcards
Sampling Error
The discrepancy, amount of error, between a sample statistic and its corresponding population parameter.
–S
Distribution of sample means
The collection of sample means for tall the possible random samples of a particular size (n) that can be obtained from a population.
Central Limit Theorem
Distribution of sample means for sample size n will have mean of mew and sd of sigma/sqrt of n, and will approach a normal distribution as n approaches infinity.
Law of Large Numbers
The larger the sample size, the closer the sample mean will be closer to the population mean.
Standard Error of the Mean
Measures the standard amount of difference between M and mew that is reasonable to expect simply by chance.
Type I Error
When treatment has no effect but you say it does.
Reject Ho but it is actually true.
False positive.
change scientific status quo.
Type II Error
Treatment has effect but you say it doesn’t.
Fail to reject Ho but it is really false.
False negative.
Less problematic bc affects are still out there to be found.
Alpha
Level of significance: probability value that is used to define the very unlikely sample outcomes if the null hypothesis is true.
One-sample z-test vs t-test
Z-test: when both mew and sigma of comparison population are known.
T-test: when sigma is not known but can be found using sample data as estimate.
One-tailed Test vs. a Two-Tailed Test
One-tailed: directional: specify either an increase/decrease in population mean score. They make a statement about the direction of the effect.
Two-tailed: does not say anything about direction of the effect, simply that it is not within the parameters of Ho.
Power
Probability that the test will reject the null hypothesis if the treatment really has an effect.
- -High N = more power
- -Stronger treatments = more power
- -One-tailed = more power
- -Bigger alpha = more power.
T-distribution vs. Normal Distribution
T-distribution: changes with degrees of freedom. As df gets very large, t-diet gets closer in hale to a nomad diet. T are more variable, tends to be flatter and more spread out.
Independence Assumption
Observations within each sample must be independent.
Normality Assumption
The two populations from which the samples are selected must be normally distributed.
Within-Subjects Study
Repeated-measures study: a single sample of individuals is measured more than once on the same dependent variable. Same subjects are used in all of the treatment conditions.
