We know that statistics can be confusing, which is why our basic statistics training course is here to help! Overseen by our Principal Statistician Matthew Stephenson, who lectured in Statistics at the University of New Brunswick, the course will walk you and your team through the foundations of statistics with a view to the life sciences.

Whether you’re new to the field of biostatistics, or just want a refresher at the start of a new project, this course will give you the grounding you need to work with confidence.

## Get in touch!

If you are interested in arranging a training course, or if you have any questions, please don’t hesitate to get in touch with our Training Coordinator, Jason Segall, using the form below:

## Statistics Training: Course Overview

This statistics training course will cover the all the fundamental concepts and tools we use every day at Quantics. From the essentials of means and standard deviations to the more advanced ideas of distributions and hypothesis testing, we’ll guide you through the most important aspects of biostatistics.

### Module 1: Introduction to Statistics I

We kick off the course with an overview of some of the most basic concepts in statistics, including a look at the different types of variables. We then examine different types of averages, with particular focus on the mean and the median, and discuss the factors behind choosing to use one metric over the other.

### Module 2: Introduction to Statistics II

Our second introductory module takes a deep dive into the crucially important world of statistical variability, with detailed looks at the range, standard deviation and coefficient of variability. We then conclude with a discussion of statistical distributions, taking particular note of the Normal distribution.

### Module 3: Comparison of Means I

We move on to look at perhaps the most important idea in statistics: statistical inference. This allows us to draw evidence-based conclusions about our data. In part one, we examine the idea of a hypothesis test, and describe how to apply this methodology to some simple examples.

### Module 4: Comparisons of Means II

We continue our look at statistical inference with a look at applying hypothesis testing to paired data, before concluding with a brief introduction to Analysis of Variance (ANOVA).

### Module 5: Regression

Our course concludes with a discussion of regression, which we use to determine the relationships between variables. This gives our statistics predictive power. We focus in particular on the basics of linear regression, including estimating model parameters and the method of least squares.

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## Compartmental PK Models: PK/PD Analysis II

In previous blogs, we’ve examined the statistics behind Non-Compartmental PK/PD analysis. The existence of the Non-Compartmental Analysis (NCA) implies the existence of compartmental analysis, and, indeed, it is among... read more →

## Bioequivalence: Interpreting the FDA Guidances for a Nasal Spray

In our blog introducing bioequivalence, we described how studies establishing bioequivalence can be a way to avoid expensive clinical trials without compromising on the safety or efficacy of a... read more →