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...

 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)...

 If you are preparing for a data science or statistical modelling role, brushing up on your hypothesis testing knowledge is of paramount importance. From understanding the difference between one-tail and two-tail tests to knowing how to interpret test statistics, this blog will guide you through the essential questions and answers on hypothesis testing. Image Source: Author 1. What is a Hypothesis Test in Statistics? Let’s start from the beginning🪙! Question: What is the main purpose of a hypothesis test in statistics? A) To calculate the mean of a dataset B) To make an inference about a population parameter based on a sample C) To visualize data distribution D) To determine the correlation between two variables Answer: B) To make an inference about a population parameter based on a sample Explanation: A hypothesis test is a statistical method that allows you to make inferences or draw conclusions about a population parameter based on a sample of data. It helps you dec...

 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 ...