chapter 13: inferential statistics

Question 1

inferential statistics

Answer 1

a research method that allows researchers to draw conclusions or infer about a population based on data from a sample.

Question 2

statistics

Answer 2

descriptive data that involves measuring one or more variables in a sample and computing descriptive summary data (ex means, correlation coefficients) for those variables.

Question 3

parameters

Answer 3

corresponding values in the population.

Question 4

sampling error

Answer 4

the random variability in a statistic from sample to sample.

Question 5

null hypothesis testing

Answer 5

a formal approach to deciding between two interpretations of a statistical relationship in a sample.

Question 6

alternative hypothesis

Answer 6

an alternative to the null hypothesis (HI). proposes that there is a relationship in the population and that the relationship in the sample reflects this relationship in the population.

Question 7

reject null hypothesis

Answer 7

a decision made by researchers using null hypothesis testing which occurs when the sample relationships would be extremely unlikely.

Question 8

retain null hypothesis

Answer 8

a decision made by researchers in null hypothesis testing which occurs when the sample relationship would not be extremely unlikely.

Question 9

p value

Answer 9

the probability of obtaining the sample result or a more extreme result if the null hypothesis were true.

Question 10

alpha

Answer 10

the criterion that shows how low a p-value should be before the sample result is considered unlikely enough to reject the null hypothesis (usually up to .05).

Question 11

statistically significant

Answer 11

an effect that is unlikely due to random chance and therefore likely represents a real effect in the population.

Question 12

practical significance

Answer 12

refers to the importance or usefulness of the result in some real-world context.

Question 13

t-test

Answer 13

a test that involves looking at the difference between two means.

Question 14

one-sample t-test

Answer 14

used to compare a sample mean (M) with a hypothetical population mean that provides some interesting standard of comparison.

Question 15

test statistic

Answer 15

a statistic (ex F, t) that is computed to compare against what is expected in the null hypothesis, and thus helps find the p value.

Question 16

critical value

Answer 16

the absolute value that a test statistic (ex F, t) must exceed to be considered statistically significant (usually .05).

Question 17

two-tailed test

Answer 17

where we reject the null hypothesis if the test statistic for the sample is extreme in either direction (positive or negative).

Question 18

one-tailed test

Answer 18

where we reject the null hypothesis only if the t score for the sample is extreme in one direction that we specify before collecting the data.

Question 19

dependent-samples t-test

Answer 19

used to compare two means for the same sample tested at two different times or under two different conditions. aka paired-samples t-test.

Question 20

difference score

Answer 20

a method to reduce pairs of scores (ex pre- and post-test) to a single score by calculating the difference between them.

Question 21

independent-samples t-test

Answer 21

used to compare the means of two separate samples (M1 and M2).

Question 22

analysis of variance (ANOVA)

Answer 22

a statistical test used when there are more than two groups or condition means to be compared.

Question 23

one-way ANOVA

Answer 23

used for between-subjects designs with a single independent variable.

Question 24

mean squares between groups

Answer 24

an estimate of the population variance and is based on the differences among the sample means.

Question 25

mean squares within groups

Answer 25

an estimate of the population variance and is based on the differences among the scores within each group.

Question 26

post hoc comparisons

Answer 26

an unplanned (not hypothesized) test of which pairs of group mean scores are different from which others.

Question 27

repeated measures ANOVA

Answer 27

compares the means from the same participants tested under different conditions or at different times in which the dependent variable is measured multiple times for each participant.

Question 28

factorial ANOVA

Answer 28

a statistical relationships in which researchers study the relationships among a large number of conceptually similar variables.

Question 29

type I error

Answer 29

a false positive in which the researcher concludes that their results are statistically significant when in reality there is no real effect in the population and the results are due to chance. in other words, rejecting the null hypothesis when it is true.

Question 30

type II error

Answer 30

a missed opportunity in which the researcher concludes that their results are not statistically significant when in reality there is a real effect in the population and they just missed detecting it. in other words, retaining the null hypothesis when it is false.

Question 31

file drawer problem

Answer 31

the problem of research results not being published that fail to find a statistically significant result. as a consequence, the published literature fails to contain a full representation of the positive and negative findings about a research question.

Question 32

open science practices

Answer 32

a practice in which researchers openly share their research materials with other researchers in hopes of increasing the transparency and openness of the scientific enterprise.

Question 33

p-hacking

Answer 33

when researchers make various decisions in the research process to increase their chance of a statistically significant result (and type I error) by arbitrarily removing outliers, selectively choosing to report dependent variables, only presenting significant results, etc. until their results yield a desirable p value.

Question 34

statistical power

Answer 34

in the research design, it means the probability of rejecting the null hypothesis given the sample size and expected relationship strength. effected by sample size and effect size.

Question 35

confidence intervals

Answer 35

a range of values that is computed in such a way that some percentage of the time (usually 95%) the population parameter will lie within that range.

Question 36

bayesian statistics

Answer 36

an approach in which the researcher specifies the probability that the null hypothesis and any important alternative hypotheses are true before conducting the study, conducts the study, and then updates the probabilities based on the data.

Question 37

replicability crisis

Answer 37

a phrase that refers to the inability of researchers to replicate earlier research findings.

Question 38

HARKing

Answer 38

hypothesizing after the results are known. a practice where researchers analyze data without an a priori hypothesis, claiming afterward that a statistically significant result had been originally predicted.