This section explains statistical concepts essential for data science, including descriptive statistics, probability distributions, hypothesis testing, correlation, regression, and inferential methods. The articles are written in simple language with examples to help learners understand statistical reasoning and analysis.
In statistical inference and data science, we rarely have access to an entire population. Instead, we observe random samples and must infer unknown population parameters, denoted by symbols such as μ (mean), σ² (variance), or θ (a generic parameter). When a single numerical value computed from the sample is used to estimate a parameter, it … Read more
Introduction: Why Interval Estimation Is Essential in Statistics Point estimation provides a single numerical value as an estimate of a population parameter. While such estimates are simple and useful, they fail to convey the uncertainty inherent in the sampling process. Two different random samples drawn from the same population rarely produce identical estimates. This variability … Read more
Introduction: The Role of Estimation in Statistical Science Statistics, as a discipline, is fundamentally concerned with learning about an unknown population based on limited information. In real-world situations, populations are often extremely large, infinite, or conceptually abstract. For example, the population may consist of all possible measurements produced by a manufacturing process, all potential voters … Read more
1. Introduction Sampling and Sampling Distribution form the backbone of modern statistical analysis. In real-world situations, collecting information from every individual in a population is rarely possible due to limitations such as time, cost, accessibility, and feasibility. Instead, analysts, researchers, and data scientists rely on samples. These samples provide information that helps us understand how … Read more
