Tag: Statistical

Most people working with data make observations and then wonder whether these observations are statistically significant. And unless one has some formal training on statistical inference and past experience in running significance tests, the first thought that comes to mind is to find a statistician...

In our previous article on why most examples of Bayesian inference misrepresent what it is, we clarified a common misunderstanding among beginners of Bayesian Statistics. That is, the field of Bayesian Statistics IS NOT defined by its use of Bayes’ Theorem but rather by its use of probabi...

P-values, oh how they confuse and confound us. These little statistical measures hold the power to determine if there is a significant difference between two groups or treatments. They range from 0 to 1, with smaller values indicating a greater confidence in the observed difference. But what do thes...

Have you ever considered how decisions are formulated under uncertainty, or how to condense large volumes of data into comprehensible formats? Imagine being a healthcare analyst tasked with spotting trends in patient recovery times across different hospitals. With hundreds of thousands of data point...

In the vast ocean of data science, where decisions are often steered by the compass of statistical analysis, the p-value stands as a beacon of inferential insight. It’s a concept that, despite its widespread application and critical importance in hypothesis testing, is often surrounded by misc...

Data processing and analysis in Java — or increasingly in web environments with Spring Boot (a popular Java framework)— is a common approach. For instance, you can run an initial statistical analysis to get valuable insight into given data or perform the pre-processing or feature extr...

A/B testing is one of the most popular controlled experiments used to optimize web marketing strategies. It allows decision makers to choose the best design for a website by looking at the analytics results obtained with two possible alternatives A and B. In this article we’ll see how ...

The bootstrap resampling method stands out as a powerful tool for hypothesis testing, especially in scenarios where traditional parametric tests may not be applicable due to the lack of normal distribution assumptions or small sample sizes. This essay delves into the essence of bootstrap resampling ...

Yesterday’s celebration of my birthday sparked an idea for a special post on the fascinating intersection of probability, statistics, and birthdays. Within the realm of probability, few puzzles are as simultaneously simple and bewildering as the Birthday Problem, a topic often misunderstood an...

“Top 10 Best Performing Economical Mobile Phones of 2022”, “Top Rated Thriller movies of All-Time in IMDB”, “Economic Prediction over next 10-year period”, “Best Restaurants near me”, “List out Top 10 Travel Destinations across Europe” &hel...

Google Trends reveals a significant surge in interest surrounding “Marketing Impact” (appendix 1.1). The term resonates in boardrooms, representing the pursuit of proving marketing’s value by measuring marketing’s effectiveness and achieving a return on investment. Th...

Most people working with data make observations and then wonder whether these observations are statistically significant. And unless one has some formal training on statistical inference and past experience in running significance tests, the first thought that comes to mind is to find a statistician...

It was the summer of 1988 when I stepped onto a boat for the first time in my life. It was a passenger ferry from Dover, England to Calais, France. I didn’t know it then, but I was catching the tail end of the golden era of Channel crossings by ferry. This was right before budget airlines and ...

Statistics is the corpus of instruments of knowledge that allow us to infer from data through tools including, but not limited to, estimation of parameters, construction of confidence intervals, and hypothesis testing to validate our assumptions. In this article, we will learn about frameworks that ...

Statistics begins by recognizing populations versus samples. Understanding this distinction is fundamental for effective statistical analysis and decision-making The first thing you do in statistics tofigure out if you’re dealing with all the data (population) or just some of it (sample...

As a data scientist, my journey into the realm of statistical analysis has taught me the paramount importance of normality tests. These tests are critical in determining whether a dataset is well-modeled by a normal distribution or not, which in turn influences the type of statistical methods that c...

Statistical insignificance Contrary to popular belief, the term “statistically significant” does not mean that something important, momentous, or convincing took place. If you think that we’re using the word significant here in a way that would make...

Hi, we are Nikita and Daniel from the CoreML team at VK. It’s our job to design and improve recommender systems for friends, music, videos and the news feed. This involves tons of A/B tests, and our progress at the end of the day relies heavily on how accurate and efficient...

While the t-tests are powerful, they are not universally applicable in the data world that is populated by business metrics with various distributions significantly different from the nice uni-modal normal distribution. For instance, the number of gifts sent and share actions per user is usually hig...