statistical data also subject error many errors classified random noise systematic bias types errors eg blunder analyst reports incorrect units also important presence missing data censoring may result biased estimates specific techniques developed address problems interval estimation confidence intervals red line true value mean example blue lines random confidence intervals realizations studies sample part population results n't fully represent
Applied science Library and museum studies Statistics