Instrumental Analysis Elementary Statistics. I. Significant Figures The digits in a measured quantity that are known exactly plus one uncertain digit.

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Presentation transcript:

Instrumental Analysis Elementary Statistics

I. Significant Figures The digits in a measured quantity that are known exactly plus one uncertain digit mL Certain mL Certain + one uncertain

I. Significant Figures Rule 1: To determine the number of significant figures in a measurement, read the number from left to right and count all digits, starting with the first non-zero digit. Rule 4: When a number is rounded off, the last digit to be retained is increased by one only if the following digit is 5 or more. Rule 2: When adding or subtracting numbers, the number of decimal places in the answer should be equal to the number of decimal places in the number with the fewest places. Rule 3: In multiplication or division, the number of significant figures in the answer should be the same as that in the quantity with the fewest significant figures.

II. Precision & Accuracy A.Precision –the reproducibility of a series of measurements. Figures of Merit Standard Deviation Variance Coefficient of Variation Good Precision Poor Precision

B.Accuracy – How close a measured value is to the known or accepted value. Figures of Merit Absolute Error Percent Error (Relative Error) Good Precision Good Accuracy Poor Accuracy

III. Types of Error E a = E s + E r Absolute error in a measurement arises from the sum of systematic (determinate) error and random (indeterminate) error. A.Systematic Error -has a definite value and an assignable cause. There are three common sources of systematic error: 1.Instrument error 2.Personal error 3.Method error

B.Random Error -Uncertainty in a measurement arising from an unknown and uncontrollable source. (Also commonly referred to as Noise.) Error fluctuates evenly in both the positive and negative direction. Treated with statistical methods.

IV. Statistical Treatment of Random Error (E r ) A large number of replicate measurements result in a distribution of values which are symmetrically distributed about the mean value. A. Normal (Gaussian) Error Curve -A plot of fraction of measurements vs. the value of a measurement results in a bell shaped curve symmetrically distributed about the mean.

A. Normal Error Curve  xixi Relative frequency, dN / N +1  -1   = 50  = 5 +1  -1   = 50  = 10  = Population Mean (True Mean)  =Population Standard Deviation Population refers to an infinite number of measurements

+3  -3  +2  -2  +1  -1  A. Normal Error Curve 68.3% of measurements will fall within ±  of the mean.  xixi Relative frequency, dN / N 95.5% of measurements will fall within ± 2  of the mean. 99.7% of measurements will fall within ± 3  of the mean.

B. Statistics 1.Population Mean (  ) x i = individual measurement N = number of measurements

B. Statistics 2.Population Standard Deviation (  ) x i = individual measurement N = number of measurements

B. Statistics 3.Population Variance (   ) Square of standard deviation Preferred by statisticians because variances are additive.

B. Statistics 4.Sample Mean (x) x i = individual measurement N = number of measurements

B. Statistics 5.Sample Standard Deviation (s) x i = individual measurement x = sample mean N-1 = degrees of freedom

B. Statistics 6.Sample Variance - s 2 7.Coefficient of Variation (CV) Percent standard deviation