BASIC STATISTICS FOR HEALTHCARE PROFESSIONALS

Who should take this course?
All healthcare professionals whom want to be able to apply statistical techniques as part of their evidence based medicine initiatives.

Pre-requisites: Basic high school math.

Course Goal: The goal is for participants to be able to apply basic statistical techniques to their own field of expertise. This 2 day course will consist of discussion of the course material, computer exercises, and applications of statistics. 

Software: Microsoft Excel will be used throughout this course. An easy to use Excel workbook containing the statistical methods presented will be given to each participant. Step by step exercises with software screen shots will be used demonstrate most statistical methods.

Course Philosophy: This course is devoted to the practical application of statistics using Microsoft Excel. The advantages of this course include: 

·   Complex statistical jargon has been reduced. 

·   Practical applications are stressed without loss of a fundamental understanding of statistical principles. 

·   Case studies will be presented. 

·   We take a very "hands-on" approach! Participants conduct many in-class exercises.

Course Content:

1.       The need for statistics: This module will provide motivation for why we need to use statistics to help us make data driven decisions. There is a fun hands-on exercise that serves as a course “ice breaker.”

2.       Statistical thinking and process basics: After completing this module participants will be able to:

·   List 3 principles of statistical thinking.

·   Create and Input Process Output (IPO) diagram.

·   Know the common terminology used for inputs and outputs.

·   Be able to identify variables as discrete, categorical, ordinal, and continuous.

3.       Displaying and describing one variable: The use of descriptive statistics and graphical tools used to describe one variable will be discussed.  The participant will be able to:

·   Calculate and interpret the mean, median, mode, range, and standard deviation.

·   Create and interpret a histogram.

·   Interpret a box plot.

·   Given a set of data, determine the direction of skewness.

·   Identify outliers.

4.       Introduction to statistical inference: Sampling is a powerful tool that can allow you to quickly estimate key process parameters without having to obtain the entire population data set. The statistical meaning of “confidence” will be examined along with confidence intervals for proportions. This module will also introduce the concept of hypothesis testing. Participants will be able to:

·   Understand the terminology sample and populations.

·   Know the statistical meaning of “confidence.”

·   Calculate and interpret a confidence interval for a proportion

·   Correctly apply the terminology; null hypothesis, alternative hypothesis, type I error, type II error, alpha, beta, and power.

5.       Estimating means and proportions: We will use sampling to help us efficiently estimate means and proportions. Participants will be able to:

·   Calculate and interpret confidence intervals for proportions.

·   Calculate and interpret confidence intervals for means.

·   Calculate the sample size needed to estimate a mean or proportion to within a desired precision.

·   Know what effects the width of a confidence interval and how.

·   Know how to apply the finite population correction factor.

·   Calculate and interpret a tolerance interval.

6.       Comparing two means and variances: This module will show participants how to identify cause and effect relationships when one variable is continuous and the other is discrete.

·   Use an F-test to test for equality of process variability.

·   Use a t-test to test for equality of process centers.

·   Create and interpret confidence intervals for the ratio of two variances.

·   Create and interpret confidence intervals for the difference of two means.

·   Calculate sample sizes needed to detect changes in process centers.

7.       Correlating two continuous variables: Describing the association between two continuous variables will be presented. Participants will be able to:

·   Build and interpret a scatter diagram in Excel.

·   Use Excel to calculate a correlation matrix.

·   Know the properties of a correlation coefficient.

·   Be able to test whether or not a correlation is statistically significant.

·   Understand how correlation can be misleading.

8.       Comparing two discrete variables: Describing the association between two discrete variables will be presented. Participants will be able to:

·   Perform and interpret a chi-square test for independence.

·   Know that a 2x2 table can be analyzed by considering a chi-square test or by looking at the difference of proportions.

·   Be able to carry out and interpret confidence intervals and hypothesis tests for the difference of two proportions.

·   Be able to perform sample size calculations for testing the difference of two proportions.