Point and Interval Estimation: Objectives: Apply the basics of inferential statistics in terms of point estimation. Compute point and estimation of population means and confidence interval. Interpret the results of point and interval estimation. Estimation: Estimating the value of parameter from the sample: An aspect of inferential statistics. Why to estimate: Population is large enough so we can only estimate. Types of estimation: Point Estimation: A specified number value (single value) that is an estimate of a population parameter. The point estimate of the population mean µ is the sample mean. Interval Estimate: Range of values to estimate about population parameter. Confidence Interval Estimation: Range of values to estimate about population parameter. May contain the parameter or not (Degree of confidence). Ranges between two values. Example: Age (in years) 4 BScN students: 20<µ < 25 or (22.5 +2.5) FORMULA: Point estimate (x) + Critical Value x Standard Error. Confidence Interval is a particular interval of estimate. Given that sample size is large, the 95% of the sample means taken from same population and same sample size will fall in + 1.96 SD of the population mean. Three commonly used Confidence Intervals are 90%, 95% (by default) , and 99%. Why not too small or too large confidence intervals? Too wide: 99.9% Interval too broad Too narrow: 80 % More uncertainty to have population mean. The 99% of the sample means taken from same population and same sample size will fall in + 2.575 SD of the population mean. Interpretation: 99% probability that interval will enclose population parameter and 1% chance that it will not have population parameter. Level of confidence: The level of certainty that the interval will have the true population mean. Chances of Error: Chances that the interval will not cater the true parameter. Sum of level of confidence and chances of error =100%