## SFU/UBC/UVic Chemistry Alumni Reception – Monday, June 2, 2014 @ Vancouver Convention Centre

I am excited to attend an alumni reception on next Monday for chemistry graduates from Simon Fraser University (SFU), the University of British Columbia (UBC), and the University of Victoria (UVic).  This event will be held as part of the 97th Canadian Chemistry Conference (CSC-2014), which will be hosted by SFU’s Department of Chemistry.  If you will attend this event, please feel free to come up and say “Hello”!

I look forward to catching up with my old professors and learn about the research that chemists across Canada are conducting!  The coordinates of this event are below; no RSVP is necessary, and the attire is business casual.

SFU/UBC/UVic Alumni Reception
Date: Monday June 2nd, 2014
Time: 6:00 to 8:00pm

Location: Room 306, Vancouver Convention Centre

## Presentation on Statistical Genetics at Vancouver SAS User Group – Wednesday, May 28, 2014

I am excited and delighted to be invited to present at the Vancouver SAS User Group‘s next meeting.  I will provide an introduction to statistical genetics; specifically, I will

• define basic terminology in genetics
• explain the Hardy-Weinberg equilibrium in detail
• illustrate how Pearson’s chi-squared goodness-of-fit test can be used in PROC FREQ in SAS to check the Hardy-Weinberg equilibrium
• illustrate how the Newton-Raphson algorithm can be used for maximum likelihood estimation in PROC IML in SAS

You can register for this meeting here.  The meeting’s coordinates are

9:00am – 3:00pm

Wednesday, May 28th, 2014

BC Cancer Agency Research Centre

675 West 10th Avenue.

Vancouver, BC

If you will attend this meeting, please feel free to come up and say “Hello!”.  I look forward to meeting you!

## Machine Learning and Applied Statistics Lesson of the Day – Sensitivity and Specificity

To evaluate the predictive accuracy of a binary classifier, two useful (but imperfect) criteria are sensitivity and specificity.

Sensitivity is the proportion of truly positives cases that were classified as positive; thus, it is a measure of how well your classifier identifies positive cases.  It is also known as the true positive rate.  Formally,

$\text{Sensitivity} = \text{(Number of True Positives)} \ \div \ \text{(Number of True Positives + Number of False Negatives)}$

Specificity is the proportion of truly negative cases that were classified as negative; thus, it is a measure of how well your classifier identifies negative cases.  It is also known as the true negative rate.  Formally,

$\text{Specificity} = \text{(Number of True Negatives)} \ \div \ \text{(Number of True Negatives + Number of False Positives)}$

## Applied Statistics Lesson and Humour of the Day – Type I Error (False Positive) and Type 2 Error (False Negative)

In hypothesis testing,

• a Type 1 error is the rejection of the null hypothesis when it is actually true
• a Type 2 error is the acceptance of the null hypothesis when it is actually false.  (Some statisticians prefer to say “failure to reject” rather than “accept” the null hypothesis for Type 2 errors.)

A Type 1 error is also known as a false positive, and a Type 2 error is also known as a false negative.  This nomenclature comes from the conventional connotation of

• the null hypothesis as the “negative” or the “boring” result
• the alternative hypothesis as the “positive” or “exciting” result.

A great way to illustrate the meaning and the intuition of Type 1 errors and Type 2 errors is the following cartoon.

Source of Image: Effect Size FAQs by Paul Ellis

In this case, the null hypothesis (or the “boring” result) is “You’re not pregnant”, and the alternative hypothesis (or the “exciting” result) is “You’re pregnant!”.

This is the most effective way to explain Type 1 error and Type 2 error that I have encountered!