Page 55 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

Type I Errors

1. Type I Error occurs when we find an effect or relationship in our
sample that is not in the population.
2. Have a significant result when in fact it is not significant.
3. Reject a true null hypothesis.
4. The probability of a Type I Error = α.
5. The conventional α value is .05.
6. Since the probability of a Type I Error is α, the researcher has
direct control over how likely a Type I Error would occur.
7. Using α= .05 means that even when no difference exists in the
population, 5% of random samples will show a significant
difference.
8. To reduce type I errors, simply use a smaller α, (i.e. .01 or .001).

Type II Errors

1. Type II Error occurs when we do not find an effect or relationship
in our sample that exists in the population.
2. Have a non-significant result when in fact it is significant.
3. Fail to reject a false null hypothesis.
4. Probability of a Type II Error = β
5. Type II Errors (β) depends on the size of the difference in the
population and the sample size.
6. A similar measure is power (1 − β), the probability that a study
will produce a statistically significant result if the research
hypothesis is true.
7. The conventional level for power is .8, this is what researchers
strive for when planning a research study.

How to increase power

1. Increase sample size
2. Intensify or prolong treatment
3. Increase the type I error rate

The Theory and Applications of Statistical Inferences 39