S101. Basic Statistics: Descriptive Statistics and Sampling, 30 CE-hours, $63
Course Description: This course provides an introduction to selected important topics in statistical concepts and reasoning. It represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data.
Objectives:
- Understand and give examples of different types of data.
- Calculate standard normal scores and resulting probabilities.
- Calculate and interpret confidence intervals for population means.
- Calculate the mean, median, mode, maximum, SD, variance, range, IQR, p, t-test, NOVA, Chi-Square, R, n, Bar-chart, Q1 of selected data.
Course Format: Online linked resources and lectures that you can use anytime 24/7. One multi-choice test.
Course Developers and Instructors: R. Klimes, PhD, MPH (John Hopkins U), author of articles on statistics, infection control and hygiene. Heather Hawkins, DMD (Nova Southeastern U), specialist in infection control and research.
Course Time: About thirty hours for online study, test taking with course evaluation feedback and certificate printing.
Professor Rudolf Klimes, PhD, welcomes you to this online course. Keep going.
START the course here. TAKE the exam at the end. PAY after the exam.
Introduction
When talking about statistics as mathematical operations, there are two basic divisions within the field: descriptive and inferential. Descriptive statistics uses graphical and numerical summaries to give a ‘picture’ of a data set. Inferential statistics, which use mathematical probabilities, make generalizations about a large group based on data collected from a small sample of that group. (Source: Dr. Fritz C. Kessler, Department of Geography, Frostburg State University. Original URL Read on the Internet Archive)
This course focuses on descriptive statistics, and on their use.
Study Lessons
Study the following lecture slides from John McGready and John Hopkins School of Public Health:
- Part 1: Describing Data
- What role does statistics have in public health?
- Types of data: continuous, binary, categorical, time-to event
- Continuous data: numerical summary measures
- Continuous data: visual summary measure
- Sample data versus population (process) level data
- Part 2: Describing Data Part II
- The normal distribution
- Means, variability, and the normal distribution
- Calculating normal (z) scores
- Means, variability and z-scores for non-normal distributions
- Part 3: Sampling Variability and Confidence Intervals
- Sampling distribution of a sample mean
- Variability in the sampling distribution
- Standard error of the mean
- Standard error vs. standard deviation
- Confidence intervals for the population mean μ
- Sampling distribution of a sample proportion
- Standard error for a proportion
- Confidence intervals for a proportion
- Part 4: An Introduction to Hypothesis Testing: The Paired t-Test
- Comparing two groups: the paired data situation
- Hypothesis testing: the null and alternative hypotheses
- Relationships between confidence intervals and hypothesis testing when comparing means
- p-values: definition, calculations, and more information
Additional Study Resources
Explore and Review Textbooks:
Use A Statistical Calculator:
Statistics Library and Resources:
Test
Course Test 6632: Click here for the self-correcting test and online payment, and receive your certificate immediately online. Test requires 75% for a passing grade.
Recommendations for you: Continuing education online courses in statistics, health and ethics. Visit our Preventing Medication Errors Class (https://cecourses.org/preventive-care/preventing-medical-errors-2/), to learn how to distinguish the common types of medical errors and how they can be prevented, improve patient safety through various procedures, learn protocols and policies that impact patients, list the common medication errors that can make a medical setting unsafe for patients, discuss the prevention of medication errors in various settings for different populations, and evaluate the various reporting systems and approaches that deal with medication errors.