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 ...

  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 ...

 In the first part of the hypothesis testing interview questions, we discussed about some of the important concepts related to hypothesis testing. Inferential statistics is a huge domain, and not possible to cover all the topics in one blog. So, in this series, we’ll continue the journey and cover other important concepts. This blog will walk you through the most important questions and answers about hypothesis testing for different scenarios involving sample sizes and known/unknown population standard deviations. Ready to ace your interview? Let’s dive in! 🌟 1. What is the Purpose of Testing the Mean in Hypothesis Testing? Let’s start with the fundamentals! Question: Why do we perform hypothesis testing on the mean in statistics? A) To determine if there is a significant difference between the population mean and a sample mean B) To find the variance of the data C) To establish a causal relationship between two variables D) To calculate the median of the dataset Answer: A)...

 Beginner-friendly introduction Hey there! If you’re diving into the world of statistics, you’ve probably come across the term “hypothesis testing.” It’s a fundamental concept that’s super useful in various fields, from science to business. But don’t worry if it sounds a bit technical. I’m here to break it down for you in simple, easy-to-understand language. Let’s jump right in! 1. What is Hypothesis Testing? Hypothesis testing is like a detective game where you start with an assumption (a hypothesis) and then collect evidence (data) to decide whether your assumption is likely to be true. It’s a way of making decisions or inferences about a population based on a sample of data. Let’s take an example: Imagine you’re a quality control manager at a factory that produces light bulbs. You claim that on an average, the lifespan of a light bulb produced by the factory is 1000 hours. Hypothesis testing will allow you to test this claim. You’d collect a sample of light bulbs, measure their...

 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 ...

Today, we’re diving deep into the Titanic dataset  — yes, the one where Jack could’ve probably fit on that door.  Our mission? To examine distribution, skewness, centering, and other properties of the dataset. No fluff — just straightforward Python code and simple explanations with a touch of humor. Let’s set sail! 1. Loading the Dataset: Meet the Titanic Passengers First, let’s import our tools and load the dataset. import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import scipy.stats as stats # Load Titanic dataset data = sns.load_dataset( 'titanic' ) data.head() Explanation:  We’re using three key libraries: Pandas: For data manipulation Seaborn: For visualization (and the Titanic dataset) Matplotlib: For displaying plots Output:  The first five rows of the dataset, featuring columns like survived , pclass , sex , age , and fare . 2. Visualizing Data Distributions Let’s start by visualizing the age distribution — because age played ...