MPC 06 December 2022 Write short notes on Point and Interval Estimation (Marks 3)

 Point and Interval Estimation                                                                                             3

Point Estimation: We have indicated that x obtained from a sample is an unbiased and consistent estimator of the population mean (μ). Thus, if an investigator obtains Adjustment Score from 100 students and wanted to estimate the value of (μ) for the population from which these scores were selected, researcher would use the value of x as an estimate of population mean (μ). If the obtained value of x were 45.0 then this value would be used as estimate of population mean (μ).

This form of estimate of population parameters from sample statistic is called point estimation. Point estimation is estimating the value of a parameter as a single point, for example, population mean (μ) = 45.0 from the value of the statistic x = 45.0

Interval Estimation: A point estimate of the population mean (μ) almost is assured of being in error, the estimate from the sample will not equal to the exact value of the parameter. To gain confidence about the accuracy of this estimate we may also construct an interval of scores that is expected to include the value of the population mean. Such intervals are called confidence interval. A confidence interval is a range of scores that is expected to contain the value of (μ). The lower and upper scores that determine the interval are called confidence limits. A level of confidence can be attached to this estimate so that, the researcher can be 95% or 99% confidence level that encompasses the population mean.