Data Science Services
HOW DO YOU EMPOWER DATA?
Statistically-Driven Experimental Design
We come in during the beginning stage of your project or trial and help design your experiments using the rigorous statistical design of experiments (DOE) approach.
DOE is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield a valid and objective conclusion. DOE begins with determining the objectives of an experiment and selecting the process factors for the study. We will make sure you’re properly powered and collecting the data in a manner that will allow for robust statistics when you get to the other side.
Statistical Fact Checking
Unfortunately, sometimes we find that the statistics performed during a project are not necessarily up-to-par. We can help by reviewing these statistical methods and providing feedback on the validity, or we can go ahead and fix them up for you so no reviewer will ever question your results. How do we prove it? By providing every line of code that we will use on your data, so that you know what happened and what decisions were made at every step of the way.
Working With Traverse
The first thing you can expect is an initial meeting to familiarize ourselves with your work.
We want to understand what motivates your study and what you are hoping to get out of your data – this way, we can tailor the perfect deliverable, timeline, and price that exceeds what you are looking for.
Once we thoroughly understand your project and vision, we create a statement of work entailing what we propose to do, the methods that will be utilized, the deliverables, and the total cost.
As a team of diverse and interdisciplinary scientists, we live and breathe data. Our expertise in nutrition, health, neuroscience, and statistics not only allows us to grasp the big picture, but to perform the rigorous and repeatable statistical modeling to back it up. We’ve dealt with everything from ANOVAs to proteomics and all that’s in-between, so we look forward to many types of challenging datasets.