X hits on this document

189 views

0 shares

0 downloads

0 comments

19 / 81

A   0.05

When a researcher uses hypothesis testing, the researcher can never be certain that the conclusion he/she draws is correct. The decisions a researcher makes versus the truth can be portrayed by the following table.

Correct Decision

Type II Error (Probability )

RESEARCHER ACCEPTS Ha

Type II Error (Probability )

Correct Decision

RESEARCHER ACCEPTS Ho

Ha True

Ho True

TRUTH

If H0 is true, but by chance the data suggested strong enough evidence against H0 to reject H0, then a type I Error has been committed. The probability of a Type I Error is the -level of the test. Therefore, if = 0.01, then only 1% of the time will data be strong enough to reject H0 when H0 is true, resulting in a Type I Error.

If Ha is true, but the evidence against H0 was not strong enough to reject H0, then a Type II Error has been committed. The power of a test is defined as the probability of rejecting H0 when Ha is in fact true (the ability of the test to correctly identify a significant difference). The power of a test is directly related to the probability of committing a Type II Error. The probability of a Type II Error is and the power of a test is given by (1 - ). One of the most common reasons for a Type II Error is due to sample size being too small. In general, the larger the sample size, the greater the power of the test.

Document info
Document views189
Page views189
Page last viewedWed Dec 07 22:59:52 UTC 2016
Pages81
Paragraphs453
Words11361

Comments