Top 10 Riffyn Science Blog Posts in 2018

1 Understanding Family-Wise Error Rate

Family-Wise Error Rate (FWER) is an approach for multiple testing correction. It is a scary sounding term, but don’t be deterred...

2 Understanding False Discovery Rate

FDR is a very simple concept. It is the number of false discoveries in an experiment divided by total number of discoveries in that experiment. But there is a problem...

3 Riffyn launches Open Access for scientists at non-profits

To celebrate the ground-breaking publication of Riffyn SDE in the journal Scientific Data, Riffyn is launching Open Access which provides free use...

4 The Most Important Scientific Calculation You Never Do

If you test tens or hundreds of samples in a single experiment, it’s guaranteed that you’ll find many false positive results, in every experiment. So what can you do about it?

5 A Better Way to Hit-Pick Strains, Enzymes, and Anything Else

Riffyn allows users to directly associate decision criteria with hit data to enable improved selection processes and ultimately smarter, faster product development.

6 Shorter Time to Market is Reshaping Biopharma R&D

Drug makers are focused on reducing the time to market for new drugs. Here we outline some of the ways they are doing that.

7 Be your own data scientist — it’s easier than you think

If you have assumed that data science is only for data scientists, then you're probably missing some important discoveries.

8 Buried in Data and Starving for Information

Since the time of Newton and Galileo, the tools for capturing and communicating science have remained conceptually unchanged. But these tools are wholly inadequate for today's science...

9 Data is Like Ikea Furniture, It’s Best Shipped Flat

The key to analyzing data faster and more accurately is in the way we record and organize our data. But those tables we all make in Excel are going to cause you problems in the long run.

10 The Importance of Measurement Systems

Your science is only as good as your ability to measure outcomes. Where should effort be placed in measurement system development?

Douglas Williams