Tech Talk with Toni

Tens of millions of people suffer from dog bites each year globally, compared to just 50–80 shark bites. As a data professional, I am expected to interpret data and provide stakeholders with insights in the form of a story. Many refer to this skill as "critical thinking." While that is valuable, one must also question how they arrived at their conclusions in the first place. According to Wikipedia, a bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is inaccurate, closed-minded, prejudicial, or unfair. I gained a deeper understanding of biases during an Organizational Behavior course I took in university, and I briefly mentioned it in Blog Post #8, which discusses data-driven decision-making. Beyond numbers in a database, biases can lead to poor decisions in the real world. For example, it is statistically more likely to be bitten by a dog than by a shark; however, availability bias caused authorities in Tobago to place a bounty ...

On May 22, 1960, in Valdivia, Chile, the largest recorded earthquake with a magnitude of 9.5 occurred. This week, I decided to refresh my understanding of the math behind the Poisson Distribution (pronounced as "pwah-son"). I first came across it during Lecture 3 of Industrial Statistics, where we were exploring Probability Distributions amidst the pandemic. Now, you might wonder, "What is a probability distribution?" In simple terms, it's a way to represent how likely an event is to happen. So, what makes the Poisson Distribution special?  It's named after the French mathematician Siméon Denis Poisson, who was fascinated by mortality rates and birth statistics.  He developed this probability distribution to model events that are both rare and random but occur at a constant average rate —think of earthquakes. Example Earthquakes occur on average 3 times per month in Little Garden Island. What is the probability that on a given month there will be no earthqu...