Introduction to Inferential Statistics

Estimator = Statistic used to estimate a population parameter Estimate = A realization of an estimator Point Estimate = A single number as the most plausible value Bayesian = Type of estimation that incorporates prior knowledge

Frequentist = Assumes parameters are fixed quantities Bayesian = Incorporates prior knowledge into estimates Point Estimate = Specific numerical value estimate Types of Estimators = Different approaches to making estimates

Estimator = Predicts unknown outcomes based on known parameters Outcome = Result of an experiment that is known after it is performed Estimate = The value derived from an estimator Parameter = Characteristic or measure of a population

<p>Estimator = Used for estimating population parameters Estimate = Represents a specific instance of an estimator Bayesian Estimation = Utilizes prior distribution for parameters Point Estimate = Identifies a single most likely value</p> Signup and view all the answers

<p>Frequentist = Estimates parameters as precisely as possible Bayesian = Predicts based on prior knowledge and outcomes Point Estimate = Focuses on a single best guess estimate Estimation = Process of inferring unknown parameter values</p> Signup and view all the answers

<p>Interval estimate = A range of numbers indicating likely containing the true value Confidence interval = An interval or range used to estimate a parameter Probability theory = The basis for decision-making in statistical inference Unbiased estimator = The expected value of estimates equals the parameter being estimated</p> Signup and view all the answers

<p>Good estimator = An estimator that consistently produces estimates close to the parameter Consistent estimator = An estimator that increases in probability of convergence to the true value Likelihood = The statement of chance expressed in values from 0 to 1 Generalization = Drawing conclusions about a population from a sample</p> Signup and view all the answers

<p>Tossing a coin = Example of probability theory Finding heads or tails = Example of statistical outcomes Long-run events = Refers to large number of experiences in a population Using samples = Basis for generalizations about the population</p> Signup and view all the answers

<p>Sample data = Basis for drawing general conclusions Population = The larger group being studied Estimation = Process of approximating a parameter Statistical inference = Making conclusions from sample information</p> Signup and view all the answers

<p>Confidence interval = Indicates where the true value is likely contained Estimator = Used to estimate parameters Sample = Subset of the population for analysis Inference = Logical conclusion drawn from data</p> Signup and view all the answers

<p>Unbiased estimator = Does not systematically overestimate or underestimate Consistent estimator = Produces more accurate estimates as sample size increases Interval estimate = Encloses a range of possible values for the parameter Point estimate = Single value estimation of a parameter</p> Signup and view all the answers

<p>Statistical inference = Drawing conclusions about population parameters Probability = Numerical expression of likelihood Generalization = Applying sample findings to wider population Researcher = Conducts studies and analyzes data</p> Signup and view all the answers

<p>Heads = Outcomes from coin tosses Tails = Alternative outcome from a coin toss Confidence interval = Reflects estimated range for a population parameter Sample size = Number of observations in a subset of the population</p> Signup and view all the answers

<p>Bayesian perspective = Parameters are treated as random variables Point estimate = Single value estimate of a parameter Confidence level = Probability that the interval estimate contains the parameter Confidence interval = Specific interval estimate based on sample data</p> Signup and view all the answers

<p>Daniel, W.W. = Biostatistics: A foundation for analysis in health sciences Nuevo, J.M. = Biostatistics and Epidemiology PowerPoint presentation Bluman, A.G. = Elementary statistics: a step by step approach Mindmover Publishing = Publisher of a biostatistics textbook</p> Signup and view all the answers

<p>Population mean = Theoretical mean of a population Sample mean = Average calculated from sample data Random variable = A variable whose values depend on the outcomes of a random phenomenon Estimation process = Method of determining parameters from sample data</p> Signup and view all the answers

<p>Confidence interval = A range of values used to estimate a parameter Confidence level = Indicates the reliability of an estimate Parameter = A characteristic or measure obtained by using data Sample selection = Process of choosing a subset from a population</p> Signup and view all the answers

<p>Random variable = Associated with probabilities assigned to outcomes Point estimate = A single best guess for a parameter Confidence level = Likelihood that an interval estimate is correct Specific interval estimate = Derives from data based on a given confidence level</p> Signup and view all the answers

<p>Consistent estimator = The value approaches the parameter as sample size increases Statistical inference = Judging the probability of estimates being close to the truth Sampling distributions = Theoretical distributions organizing statistical outcomes Point estimates = Methods for estimating parameters from sample data</p> Signup and view all the answers

<p>Method of Moments = Simplest approach for constructing an estimator Maximum Likelihood = Outcome is unknown before an experiment Efficiency = Estimator with smallest variance Estimator = Statistic used to estimate a parameter</p> Signup and view all the answers

<p>Estimator efficiency = Relative efficiency among possible estimators Outcome probability = Determined by sampling distributions Parameter estimation = Close approximation through consistent estimators Variance = Measure of the dispersion of an estimator's values</p> Signup and view all the answers

<p>Best estimators = Not necessarily obtained through method of moments Inferences = Based on statistical data from samples Sample size = Affects the performance of consistent estimators Theoretical distributions = Help derive probabilities from sample outcomes</p> Signup and view all the answers

<p>Method of Moments = Advantage of being simple Maximum Likelihood = Focuses on unknown outcomes before experiments Efficiency of an estimator = Seeks to minimize variance Sampling distributions = Estimate relative frequency in populations</p> Signup and view all the answers

<p>Consistent estimator = Connects sample size to parameter approximations Probability = Judged through statistical inference Statistical outcomes = Organized by sampling distributions Point estimates = Methods to extract information from samples</p> Signup and view all the answers

<p>Statistical inference = Allows for judgment about parameter truth Sampling distributions = Developed to structure various outcomes Point estimates = Approaches used in estimation Estimator = Statistic aimed at parameter estimation</p> Signup and view all the answers

<p>Consistent estimator = Approaches parameter as sample increases Statistical inference = Judges estimate accuracy Method of Moments = Simple but not optimal Maximum Likelihood = Uncertain outcomes pre-experiment</p> Signup and view all the answers