Interpreting Models in Research

These flashcards cover key concepts and vocabulary related to interpreting regression models and statistical significance as discussed in the lecture.

Raw Parameter Estimate (b)

Indicates the change in the outcome for a 1 unit increase in the predictor, holding everything else constant.

Standardized Parameter Estimate

Transforms variables into standard deviations, allowing comparisons across different scales.

Confidence Interval (CI)

A range of plausible values for the true effect based on the data; reflects the uncertainty around the estimate.

P-value

A measure to determine the significance of results; indicates the probability of observing the data if the null hypothesis is true.

Null Hypothesis (H0)

The hypothesis stating that there is no effect, or that the true slope b equals 0.

Effect Size

A quantitative measure of the magnitude of a phenomenon; essential for understanding the importance of the relationship.

Statistical Significance

A determination of whether a result is likely due to chance; often assessed using the p-value.

Practical Significance

The real-world relevance or importance of an effect, beyond just statistical significance.

Binary Thinking in Statistics

The flawed approach of categorizing results simply as significant or non-significant, ignoring the nuances involved.