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A confidence interval is a range of values, derived from sample data, that is likely to contain the true population parameter with a specified level of confidence. It provides a measure of uncertainty around the estimate, allowing researchers to make inferences about the population with a known level of risk for error.
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Confidence intervals provide a range of values, derived from sample data, that is likely to contain the true population parameter with a specified level of confidence. They are crucial in inferential statistics as they account for sampling variability and help in making informed decisions based on data analysis.
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Parametric estimation involves using statistical methods to estimate the parameters of a probability distribution that best fit a given data set. It assumes a specific form for the distribution and uses sample data to infer the values of the distribution's parameters, enabling predictions and insights about the underlying population.
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An empirical distribution represents the frequency or probability of outcomes based on observed data, rather than theoretical assumptions. It serves as a practical tool for understanding real-world phenomena by summarizing data points into a distribution that reflects actual occurrences.
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