Research Exam 1: Set 3

Front 1

Statistical Significance (definition)

Back 1

indicates results of an analysis showing any difference or relationships are unlikely to be the result of chance

Front 2

Statistical Significance (standard industry practice)

Back 2

p<0.05

Front 3

Type I Error

Back 3

mistakenly concluding that a real difference exists, when the difference is due to chance

Front 4

Type II Error

Back 4

mistakenly concluding that a difference is due to chance when the samples represent different populations

Front 5

Alpha Level (definition)

Back 5

level of probability predetermined by the researcher that sets the acceptable level of probability for committing a Type I error (p<0.05 usually)

Front 6

Alpha Level (use)

Back 6

used to determine if the groups are significantly different based on actual probability level (p value) as calculated from a given stats test

Front 7

(p) Value

Back 7

determined from a ratio based on mean group differences and variability

ratio = difference between group means/variability within groups

bigger ration = smaller p value = good

Front 8

Statistical Power (def)

Back 8

probability that a test will detect a difference when one actually exists

probability that a test will lead to rejection of the null hypothesis

Front 9

Statistical Power (factors)

Back 9

alpha level of at least p<0.05
maximize bt group differences
reduce variability
incr sample size
use of effect size

Front 10

Effect Size (def)

Back 10

used to help determine an adequate sample size (n) from pilot data, to protect against Type II errors

d = (x1-x2)/sd = effect size index

Front 11

Effect Size (value)

Back 11

small effect = 0.20
medium = 0.50 (effect is half of std dev)
large effect = 0.80

80% is industry standard

Front 12

What is an 0.80 effect size?

Back 12

An 80% chance that we would detect a difference between the samples if one actually existed.

Front 13

Confidence Interval (CI)

Back 13

boundaries of the confidence interval are based on sample mean and its standard error

wide: greater uncertainty about the true value of population mean

narrow: more certainty about the population mean

Front 14

Confidence Interval (calculation)

Back 14

95% CI = mean +- (95% z-score) (standard error of mean)