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Inferential Analysis

Inferential analysis is a statistical method used to draw conclusions about a population based on data from a sample. It involves making predictions, testing hypotheses, and estimating parameters using probability theory and statistical models. This approach is fundamental in data science, research, and decision-making processes where full population data is unavailable.

Also known as: Statistical Inference, Inferential Statistics, Hypothesis Testing, Parametric Estimation, Data Inference
🧊Why learn Inferential Analysis?

Developers should learn inferential analysis when working with data-driven applications, such as in machine learning, A/B testing, or business intelligence tools, to make reliable predictions and validate assumptions. It is crucial for roles involving data science, analytics, or research, as it enables evidence-based decision-making and reduces uncertainty in conclusions drawn from limited data.

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