Understanding Statistical Significance and Errors

Statistical Significance

Trustworthiness of findings based on sample data.

Population Estimation

Using samples to infer characteristics of a population.

Less than 5% Chance

Indicates results are unlikely due to random chance.

Type I Error

False positive; incorrectly rejecting true null hypothesis.

Type II Error

False negative; failing to reject false null hypothesis.

Alpha Level

Threshold for Type I Error, commonly p<.05.

Strict Alpha Levels

p<.01 and p<.001 for stricter Type I Error control.

Pearson's Correlation (r)

Measures strength and direction of linear relationship.

Sig Value

Indicates significance level in statistical output.

SPSS Stars

Visual indicators for significant correlations in output.

Reporting p Values

Use best p value based on Sig value.

p<.05

Standard significance threshold for most research.

p<.01

Stricter significance threshold indicating stronger evidence.

p<.001

Very strict significance threshold indicating very strong evidence.

False Positive Example

Incorrectly diagnosing COVID-19 when not infected.

False Negative Example

Missing cancer diagnosis when treatment was effective.

Research Defaults

Most studies use p<.05 for significance.

Significance in Correlations

Determines if correlation results are statistically meaningful.

SPSS Printout

Output format showing significance and correlation results.

Best p Value Reporting

Select lowest p value that accurately reflects significance.