# PSYC 210 Chapter Notes -Type I And Type Ii Errors, Statistical Power, Null Hypothesis

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Published on 14 Oct 2012

Department

Psychology

Course

PSYC 210

Professor

Chapter 6 Vocabulary 1

Decision errors – incorrect conclusion in hypothesis testing in relation to the real but unknown situation despite

doing the correct calculations and steps

Type I error – alpha () – rejecting the null hypothesis when in fact it is true; getting a statistically

significant result when in fact the research hypothesis is not true

Type II error – beta () – failing to reject the null hypothesis when in fact it is false; failing to get a

statistically significant result when in fact the research hypothesis is true

---------------------------DECISION ERRORS---------------------------

Real Situation (in practice, unknown)

Null Hypothesis True

Research Hypothesis True

Conclusion Using

Hypothesis-Testing

Procedure

Research hypothesis

supported (reject null)

Type I error -

Correct decision

Study is inconclusive (do not

reject null)

Correct decision

Type II error -

Effect size () – in studies involving means of one or two groups, measure of difference (lack of overlap) between

populations; the usual standardized effect size measure increases with greater differences between means and

decreases with greater standard deviations in the population, but isn’t affected by sample size; the difference

between the population means divided by the population’s standard deviation

Effect size conventions – standard rules about what to consider a small, medium, and large effect size, based on

what is typical in psychology research; also known as Cohen’s conventions

Description

Effect Size

Small

.20

Medium

.50

Large

.80

Meta-analysis – statistical method for combining effect sizes from different studies

Statistical power – probability that a study will give a significant result if the research hypothesis is true

* INFLUENCES ON POWER *

Feature of the Study

Increases Power

Decreases Power

Effect size (d) (d = [µ1 - µ2]/δ)

Large d

Small d

Effect size combines the following two features:

- Predicted difference between

population means (µ1 - µ2)

Large differences

Small differences

- Population standard deviation (δ)

Small δ

Large δ

Sample size (N)

Large N

Small N

Significance level

Lenient, high (.05 or .10)

Extreme, low (.01 or .001)

One-tailed vs. two-tailed test

One-tailed

Two-tailed

Type of hypothes-testing procedure used

Varies

Varies

Power table – table for a hypothesis-testing procedure showing the statistical power of studies with various effect

sizes and sample sizes

## Document Summary

Decision errors incorrect conclusion in hypothesis testing in relation to the real but unknown situation despite doing the correct calculations and steps. Type i error alpha ( ) rejecting the null hypothesis when in fact it is true; getting a statistically significant result when in fact the research hypothesis is not true. Type ii error beta ( ) failing to reject the null hypothesis when in fact it is false; failing to get a statistically significant result when in fact the research hypothesis is true. Effect size conventions standard rules about what to consider a small, medium, and large effect size, based on what is typical in psychology research; also known as cohen"s conventions. Meta-analysis statistical method for combining effect sizes from different studies. Statistical power probability that a study will give a significant result if the research hypothesis is true. Effect size (d) (d = [ 1 - 2]/ ) Predicted difference between population means ( 1 - 2)