Experimental measurements usually have some uncertainty which can be introduced by random error or by systematic error (or bias). Random errors show up as differences in the measurement values, and the extent to which a set of measurements agree with one another is called the precision of the measurements. Systematic errors affect the accuracy of the measurement, and must be analyzed by calibrating the measurement with samples of a known value. Statistics presents a variety of ways of describing and analyzing these errors, and determining the confidence one can have that the "true value" lies within a certain range of values. The concept of standard deviation is a woy of treating random errors that assumes the distribution of the total population of measurements follows a "normal" distribution curve (sometimes called a "bell curve"). A set of measurements is usually only a sample of the total "population" of measurements that might be made, so the formulas in the fight hand column above apply. You are asked to calibrate a 10mL volumetric pipette by weighing to the nearest 0.1mg the moss of water delivered by the pipette. You weigh six samples of water delivered by the pipette and convert the mass of each to volume by multiplying by the volume of 1.0000g of water at 25C(1.0040mL). Following are your measurements: Calculate the following statistical measures for this dato: Mean (x)= Variance (52)= Standard Deviation (5)=