Inferential Statistics

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1
used when a researcher wants to infer, or conclude/suggest from evidence and reasoning, something about a larger population based on a sample used in a study
inferential statisitcs
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inferential statisitics are often divided into two categories
tests of differences (t-test and analysis of variance)
tests of relationships (correlations and regressions)
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assume that the distribution of scores in the sample are normally distributed
- utilize means and standard deviations
parametric
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statisitics are distribution free
- can compare categorical and rank order data
- appropriate when comparing differences in percentages for groups
nonparametric
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is a number that expresses the probability that the result of a given experiment or study could have occurred purely by chance.
statistical significance
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is the amount of risk you are willing to assume when conducting a study.
level of significance (a)
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what is the level of significance used by most researchers
0.05 or 5%
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Statistical significance is reported as a _________, an abbreviation for _________.
p value
probability
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how likely something is to happen a
probability
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the most basic of inferential difference statistics because it examines the difference between two groups at a single point in time, or one group at two time points.
t-test
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(also known as a two-group/two-sample t-test or an unpaired sample t-test) compares the difference in the mean score for two unrelated groups (between-group comparison).
t-test for independent samples
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(also referred to as a paired-sample t-test) compares the pretest and posttest scores of a single group (within group comparison).
t-test for dependent samples
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the test for comparing more than two means.
analysis of variance (ANOVA)
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(similar to the between-group t-test) compares three or more groups on a single dependent measure.
one way ANOVA
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(similar the within-group t-test) is used when the means are compared over three or more time points.
repeated measures ANOVA
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is used when between- and within-group analyses are conducted simultaneously (two or more groups are compared over two or more time points).
mixed model ANOVA
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used when a researcher wants to statistically control a variable that could affect the outcome of a study.
analysis of covariance (ANCOVA)
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Useful when a demographic variable differs between groups and when that demographic variable is related to the outcome variable (dependent variable)
ANCOVA
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used when the assumptions for parametric statistics are not met: the data is not normally distributed (this requires a sample size of 30 or more participants)
nonparametric statistics
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determines if there is a statistically significant difference between frequencies in two or more groups.
chi square (X2)
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compares two groups and is used when the parametric assumptions are not met (comparable to the independent sample t-test).
Mann-Whitney test
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similar to the Mann-Whitney test, but it is used when three or more groups are compared (nonparametric equivalent of the one-way ANOVA).
Kruskal Wallis test
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describes the magnitude or strength of a statistic.
effect size
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measures the difference between two group means reported in standard deviation units
- More commonly used with t-test
Cohen's d
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____ squared (n2) and _____ squared (w2) are other common ES statisitcs.
Eta and omega
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Whenever a statistic is calculated, the result reflects the statistic of the sample, but it is an ___________ estimate of the population.
imperfect
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is a reliability estimate that suggests the range of outcomes expected for the population.
confidence interval
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provides a range of numbers in which the true score of the population likely falls
confidence interval
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CI small range =
CI large range =
small = more precise
large = less precise, more uncertainty
30
provides information about the likelihood of detecting a difference if it exists.
power
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