Elaine Eisenbeisz, Owner and Principal Statistician of Omega Statistics, is available for training and workshops in statistics, clinical research, process control/quality assurance, and other topics related to statistics and good research practices. Please call or email the Omega Statistics offices to request the latest brochure and pricing for the event(s) that interests you.
Current offerings and scheduled events are listed below.
In addition to the current offerings listed below, Elaine can design an in-house training or virtual webinar to meet your specific needs. Just give her a call at 877-461-7226 or fill out the contact form on this page to leave her a message and she’ll contact you to schedule a time to discuss your needs and how she can help!
NOTE: In-person events are not being scheduled at this time
Omega Statistics has contracted with 3rd party companies to present the following fee based events. The events are listed in chronological order. Please click on the links that interest you to learn more and to register.
May 18, 2022
Do you become tongue tied when explaining the meaning of a p-value? Would you like to know why the null hypothesis is so important to research? Why don’t studies prove anything? Are your pretty sure about what you want to say in plain English, but you’re not sure how to say it statistically?
This webinar will briefly review the history of scientific method. We will explore the steps involved in developing a research question that can be tested with statistical hypotheses. Examples of research questions and hypotheses that can and cannot be tested will be presented.
A brief lesson in statistical theory will explain why we don’t prove anything in research, we can only make really, really, good guesses…providing we look at the problem the right way. We will also discuss three ways of interpretation that in combination can be used for better decision making, namely, p-values, effect sizes, and confidence intervals.
May 25, 2022
Many of the commonly used statistical tests and calculations of chart limits (or other measurements) require that the data be “normally distributed”. This webinar will show you how to check for normality in your data and apply transformations to non-normal data. You will also learn tools and concepts to understand when a transformation of data is, or is not, necessary.
Learn the theory and concepts of determining when a normal distribution is needed, how to transform data that is not normal, and what to do when transformation does not work.
Biostatistics for the Non-Statistician
US Based Event: June 15-17, 2022 (9 a.m. – 2 p.m. Pacific Time, Daily)
Statistics is a useful decision-making tool in the clinical research arena. When working in a field where a p-value can determine the next steps on development of a drug or procedure, it is imperative that decision makers understand the theory and application of statistics.
Many statistical softwares are now available to professionals. However, these softwares were developed for statisticians and can often be daunting to non-statisticians. How do you know if you are pressing the right key, let alone performing the best test?
This seminar provides a non-mathematical introduction to biostatistics and is designed for non-statisticians. And it will benefit professionals who must understand and work with study design and interpretation of findings in a clinical or biotechnology setting.
The focus of the seminar is to give you the information and skills necessary to understand statistical concepts and findings as applies to clinical research, and to confidently convey the information to others.
Emphasis will be placed on the actual statistical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.
June 22, 2022
Verification and validation studies of design-outputs and/or manufacturing processes are required in many manufacturing processes. However, it can be difficult to understand the rational for same sizes used in these contexts. This webinar will be useful to those interested in learning how to make and justify the reasoning behind sample size determination.
This webinar provides the logic and processes for determining samples sizes for common tests used in verification or validation of processes. The focus of this webinar is on providing the information needed for attendees to know the appropriate measures and formulas to use for the determining sample size and providing justification for the planned sample sizes.
Learn the theory, terminology, regulatory requirements, best practices, and of course, the steps for calculating sample sizes for process verification and validation.
NOTE: This webinar does not address rationales for sample sizes used in clinical trials.
June 23, 2022
The power of your study is the probability that you will find a statistically significant difference or relationship (an “effect”) if that difference or relationship (effect) truly exists in the population.
A study with too small of a sample size is under-powered. This means that even if the effect you are testing for truly exists, you won’t achieve statistical significance. You will waste time by collecting a sample that is too small to properly power a study. Why perform a research if you can’t see significance for your desired effect?
A study with too large of a sample is over-powered. This means that you’ve collected such a large sample that you will see significance even on very small effects. However, the costs of subject recruitment, data collection, and follow-up (if needed) are quite large. Recruiting more subjects than needed unnecessarily inflates the temporal and monetary costs.
Questions related to the feasibility of a study can be answered by power analysis:
In this webinar attendees will learn the statistical power analysis and techniques for determining sample size (a priori techniques) calculation. Also attendees will get work examples in the free to use G*Power software. Some code and demonstrations will be provided for powering studies and performing power analysis simulations in R software.