Confidence Interval (CI)
A range of values based on a sample that is expected to include a population parameter (e.g., mean) a certain percentage of the time.
Based on the mean and standard deviation of the sample to make inferences about the population.
General Equation for Confidence Interval (CI)
- Determine the sample mean (x bar)
- Find the standard deviation
- Determine the sample size
- Choose your confidence interval (90%, 95%, or 99%)
- Find the z score (or t score that is for the value)
- Calculate the standard error (which is the standard deviation divided by the square root of the sample size)
- Calculate the margin of error which is the z (or t score multiplied by the standard error that we found in step six)
- Find the lower limit (mean-margin of error) and the upper bound (mean + margin of error)
Look at the slide for the specific equation
Confidence Level to Z-Score
90% ————-> 1.645
95%————–> 1.96
98%————–> 2.33
99%————–> 2.575
Therefore, as the confidence interval increases, so does the confidence that the true population value is contained within that interval.
Effect Size
The measure of the strength of the relationship between two variables in a population, or the difference between groups.
To determine the size of the effect of the IV on DV, we must calculate the magnitude of effect size
Magnitude of Effect Size
Ratio relating to the variability of the dependent variable attributed to the treatment (independent variable)
Treatment Magnitude: Treatment Variabiality/Total Y Variability
How do we determine which measure for effect size to use?
Relationship between two variables > Pearson’s correlation coefficient
Two group means ———> Cohen’s D
Binary Outcomes ———–> Odds ratio
ANOVA ————————-> eta squared or w squared
