Decision Science

 Ever wondered how to statistically compare two groups when you already know the population standard deviation? That’s where the two-sample Z-test comes in — especially handy for large datasets. In this step-by-step guide, we’ll walk through a fully solved numerical example that shows exactly how this test works in practice. Whether you’re prepping for a stats exam, brushing up on hypothesis testing, or applying it in real-world data analysis, this breakdown will make the concept click — without overwhelming jargon. Lets start with the problem statement! Problem Statement: A researcher wants to compare the average daily calorie intake of male and female adults in a city. Two independent random samples are taken: Sample 1 (Males): n1=40, mean X1=2500, population standard deviation σ1=300 Sample 2 (Females): n2=35, mean X2=2300, population standard deviation σ2=250 At 5% level of significance , test whether there is a significant difference in the mean calorie intake between ...

 Understanding probability distributions is essential for anyone working in data science , statistics , or machine learning . In this blog, we’ll break down three of the most common distributions  —  Normal , Binomial , and Poisson  — along with easy-to-follow Python examples using real-world data. Whether you’re building a predictive model or analyzing data patterns, mastering these distributions will sharpen your skills. Let’s dive in! What Are Probability Distributions? A probability distribution describes how the values of a random variable are distributed. It tells you the probability of different outcomes — kind of like a weather report, but for data! There are two broad types: Discrete distributions : Deal with countable outcomes (e.g., number of cars). Continuous distributions : Deal with outcomes that can take any value within a range (e.g., height, weight). 1. Normal Distribution — The Bell Curve Superstar What is it? The Normal distribution is a continuous distri...

  This is the third part of most frequently asked interview questions and answers, along with explanations on Hypothesis Testing. You can read the first two parts here: Frequently Asked Hypothesis Testing Questions for Data Scientist Interviews (part 1) Frequently Asked Hypothesis Testing Interview Questions for Aspiring Data Scientists (Part 2) We’ve covered a long journey, and it will continue in this guide too, which will cover key interview questions and answers on hypothesis testing, focusing on topics like testing means with two independent samples, one-sample proportion tests, two-proportion tests, and even how to implement these tests in Python. Ready to boost your hypothesis testing knowledge? Let’s dive in! 🚀 1. What is the Purpose of Hypothesis Testing in Statistics? Let’s start with the revision! Question: What is the main purpose of hypothesis testing in statistics? A) To confirm a theory by providing absolute proof B) To calculate correlation coefficients. C) To ...

 Credit Risk and Statistical Distributions Scenario Imagine you’re a data scientist at a lending institution, and you’ve been asked to understand and predict certain events, like the likelihood of loan defaults or the frequency of inquiries a borrower makes in a given period. This is where statistical distributions, like the Binomial and Poisson distributions, come into play. Steps: Load and Explore the Loan Dataset Understand the Binomial Distribution Implementing the Binomial Distribution in Python Understand the Poisson Distribution Implementing the Poisson Distribution in Python Step 1: Load and Explore the Loan Dataset Start by loading the dataset and taking a quick exploratory glance. import pandas as pd # Load the dataset loans_data = pd.read_csv( 'loansdata.csv' ) # Check the first few rows of the dataset loans_data.head() Output: Understand the Data The original data used in this exercise comes from publicly available data from LendingClub.com , a website that ...