# Power and Sample Size

### Member Training: Power Analysis and Sample Size Determination Using Simulation

July 30th, 2018 by
This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:
• A review of basic concepts of statistical power and effect size
• A simulation-based approach to power analysis
• An overview of how to implement simulations in various popular software programs.

### Member Training: The Fundamentals of Sample Size Calculations

May 28th, 2018 by

Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe that’s just me.

Regardless of how they make you feel, they are super important to do for your study before you collect the data.

### Sample Size Estimation Without Past Reliable Pilot Data or Evidence

March 20th, 2017 by

Here’s a common situation.

Your grant application or committee requires sample size estimates.  It’s not the calculations that are hard (though they can be), it’s getting the information to fill into the calculations.

Every article you read on it says you need to either use pilot data or another similar study as a basis for the values to enter into the software.

You have neither.

No similar studies have ever used the scale you’re using for the dependent variable.

And while you’d love to run a pilot study, it’s just not possible.  There are too many practical constraints — time, money, distance, ethics.

What do you do?

### Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?

March 1st, 2017 by

There are many rules of thumb in statistical analysis that make decision making and understanding results much easier.

Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends?

In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis, like:

### Two Types of Effect Size Statistic: Standardized and Unstandardized

October 25th, 2016 by

Effect size statistics are all the rage these days.

Journal editors are demanding them. Committees won’t pass dissertations without them.

But the reason to compute them is not just that someone wants them — they can truly help you understand your data analysis.

### What Is an Effect Size Statistic?

When many of us hear “Effect Size Statistic,” we immediately think we need one of a few statistics: Eta-squared, Cohen’s d, R-squared.
And yes, these definitely qualify. But the concept of an effect size statistic is actually much broader. Here’s a description from a nice article on effect size statistics:

“… information about the magnitude and direction of the difference between two groups or the relationship between two variables.

– Joseph A. Durlak, “How to Select, Calculate, and Interpret Effect Sizes”

If you think about it, many familiar statistics fit this description. Regression coefficients give information about the magnitude and direction of the relationship between two variables. So do correlation coefficients. (more…)

### Member Training: Small Sample Statistics

August 1st, 2016 by

Despite modern concerns about how to handle big data, there persists an age-old question: What can we do with small samples?

Sometimes small sample sizes are planned and expected.  Sometimes not. For example, the cost, ethical, and logistical realities of animal experiments often lead to samples of fewer than 10 animals.

Other times, a solid sample size is intended based on a priori power calculations. Yet recruitment difficulties or logistical problems lead to a much smaller sample. In this webinar, we will discuss methods for analyzing small samples.  Special focus will be on the case of unplanned small sample sizes and the issues and strategies to consider.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.