Confidence Intervals

Creating Confidence Intervals

The use of confidence intervals is in part, due to the fact that the traditional and restricted framework of statistical significance testing has not been universally endorsed, therefore creating the need for confidence intervals.

This comes down to a simple question, "Is it possible to assert something positive and tangible about the means of the groups in an experimental study?"

Instead of using significance level in a study, it maybe more beneficial to use a confidence interval (which is the opposite of the significance level).

For example, saying "the 6 month survival rate wan increased by 30 percentage points with a 99% confidence interval" than by simple saying the difference between the control group and experimental group was significant at the .01 level.

The creation of the confidence interval then, becomes the percentage remaining from the significance level. In this this case 100-1= 99%

contributed by Mykal Kuslis, WCSU Cohort 8

Reference:

Meyers, S., Gamst, G., & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications. (p.24-25)

How to organize an Inference Problem : The Four-Step Process

STATE: State the parameter you want to estimate and the confidence level.

PLAN: Identify the appropriate inference method and check conditions.

DO: If the conditions are met, perform calculations.

CONCLUDE: Interpret your interval in the context of the problem.

Reference:

Daren, S. S., & Tabor, J. (2020). Updated version of the practice of Statistics (Teachers Edition) (Sixth Edition). W H FREEMAN & CO LTD.

contributed by Katie Ciskowski