This is the starting point. Srivastava meticulously explains how to calculate a single "best guess" of a population parameter. Key highlights include:
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Statistical inference addresses a deceptively simple question: How can we know something about a whole (the population) when we have only seen a part (the sample)? The answer is never certain; it is always probabilistic. Srivastava’s approach, as with standard texts, begins by distinguishing between descriptive statistics (summarizing the sample) and inferential statistics (generalizing to the population). The bridge between them is probability theory. Without random sampling and an understanding of sampling distributions, inference collapses into guesswork. This is the starting point
The first major pillar of inference is estimation, which comes in two forms: point estimation and interval estimation. A point estimate, such as the sample mean (\barx), serves as a single best guess for a population parameter (\mu). However, as Srivastava likely emphasizes, a point estimate is almost never exactly correct. Hence, we construct confidence intervals—ranges of plausible values that capture the true parameter with a specified level of confidence (e.g., 95%). The logic of the confidence interval reveals a key insight: inference is not about certainty but about managing uncertainty. The answer is never certain; it is always probabilistic