It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.
It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.
Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.
Have you ever experienced befuddlement when you dust off a data analysis that you ran six months ago?
Ever gritted your teeth when your collaborator invalidates all your hard work by telling you that the data set you were working on had “a few minor changes”?
Or panicked when someone running a big meta-analysis asks you to share your data?
If any of these experiences rings true to you, then you need to adopt the philosophy of reproducible research.
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:
One of the biggest challenges that data analysts face is communicating statistical results to our clients, advisors, and colleagues who don’t have a statistics background.
Unfortunately, the way that we learn statistics is not usually the best way to communicate our work to others, and many of us are left on our own to navigate what is arguably the most important part of our work.
In this webinar, we will cover how to: (more…)
In this webinar, we’ll discuss when tables and graphs are (and are not) appropriate and how people engage with each of these media.
Then we’ll discuss design principles for good tables and graphs and review examples that meet these principles. Finally, we’ll show that the choice between tables and graphs is not always dichotomous: tables can be incorporated into graphs and vice versa.
Participants will learn how to bring more thoughtfulness to the process of deciding when to use tables and when to use graphs in their work. They will also learn about design principles and examples they can adopt to create better tables and graphs.
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.