University:
Kentucky State University
Course:
CHE 102 | General Chemistry II
Academic year:

2019
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371

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30
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salcignofi17

Chapter 6 Random Error The Nature of Random Errors Random, or indeterminate, errors occur whenever a measurement is made. This type of error is caused by the many uncontrollable variables that are an inevitable part of every physical or chemical measurement. There are many contributors to random error, but often we cannot positively identify or measure them because they are small enough to avoid individual detection. The accumulated effect of the individual random uncertainties, however, causes the data from a set of replicate measurements to fluctuate randomly around the mean of the set. Distribution of Experimental Results Frequency distribution Sources of Random Errors  Sources of random uncertainties in the calibration of a pipet include: (1) visual judgments, such as the level of the water with respect to the marking on the pipet and the mercury level in the thermometer; (2) variations in the drainage time and in the angle of the pipet as it drains; (3) temperature fluctuations, which affect the volume of the pipet, the viscosity of the liquid, and the performance of the balance; and (4) vibrations and drafts that cause small variations in the balance readings.  Undoubtedly, numerous other sources of random uncertainty also operate in this calibration process.  It is difficult or impossible to determine the influence of any one of the random errors arising from these variables, but the cumulative effect is responsible for the scatter of data points around the mean. Results of coin flipping Samples and Populations A finite (‫ )محدود‬number of experimental observations is called a sample of data. The sample is treated as a tiny fraction of an infinite number of observations that could in principle be made given infinite time. Statisticians call the theoretical infinite number of data a population, or a universe, of data. Statistical laws have been derived assuming a population of data; often, they must be modified substantially when applied to a small sample because a few data points may not be representative of the population. The Population Mean  and the Sample Mean x Statisticians find it useful to differentiate between a sample mean and a population mean. The sample mean x is the mean of a limited sample drawn from a population of data. x  The population mean , in contrast, is the true mean for the population. In the absence of any systematic error, the population mean is the true value for the measured quantity. N x i i 1 , when N is small N N x i   i 1 N , when N   To emphasize the difference between the two means, the sample mean is symbolized by x and the population mean by  . When N is small, x differs from  because a small sample of data does not exactly represent its population. The probable difference between x and  decreases rapidly as the number of measurements making up the sample increases; ordinarily, bythe time N reaches 20 to 30, this difference is negligibly small. The Population Standard Deviation() The population standard deviation , which is a measure of the precision or scatter of a population of data, is given by the equation N  x i   i 1 2 where: N is the number of data points making up the population.   N variance : The square of the standard deviation = 2 N 2   (   ) 2 i 1 N The standard deviation has the same units as the data, whereas the variance has the units of the data squared. It is easier to relate a measurement and its precision if they both have the same units. The advantage of using variance is that variances are additive. Sample Standard Deviation Standard Deviation equation must be modified when it is applied to a small sample of data. Thus, the sample standard deviation s is given by the equation N s  ( x  x) N 2  i 1 N 1 d i 1 2 i N 1 This equation differs from the standard deviation equation in two ways. First, the sample mean, x, appears in the numerator of sample standard deviation equation in place of the population mean, . Second, N in standard deviation equation is replaced by the number of degrees of freedom (N –1). The sample variance s2 is also of importance in statistical calculations. It is an estimate of the population variance 2. Variance (s2) N s2   ( x  x) i 1 N 1 N 2   di 2 i 1 N 1 Normal error curves The two normal error curves in Fig. 6-4 are for two populations of data that differ only in their standard deviations. The standard deviation for the data set yielding the broader but lower curve B is twice that for the measurements yielding curve A. the breadth of these curves is a measure of the precision of the two sets of data. Thus, the precision of the data leading to curve A is twice as good as that of the data that are represented by curve …continued… Fig. 6-4b shows another type of normal error curve in which the abscissa is now a new variable z, which is defined as, z = (x - ) /  z is the deviation of a data point from the mean relative to one standard deviation. That is, when x -  = , z is equal to one; when x -  =2, z is equal to two; and so forth. A plot of relative frequency versus this parameter yields a single Gaussian curve that describes all populations of data regardless of standard deviation. A normal error curve has several general properties: (1) The mean occurs at the central point of maximum frequency, (2) there is a symmetrical distribution of positive and negative deviations about the maximum, and (3) there is an exponential decrease in frequency as the magnitude of the deviations increases. Thus, small random uncertainties are observed much more often than very large ones. Areas under a Gaussian Curve It can be shown that, regardless of its width, 68.3% of the area beneath a Gaussian curve for a population of data lies within one standard deviation (1) of the mean . Thus, 68.3% of the data making up the population will lie within these bounds. Furthermore, approximately 95.4% of all data points are within 2 of the mean and 99.7% within 3. Relative Standard Deviation (RSD) and Coefficient of Variation (CV) We calculate the relative standard deviation by dividing the standard deviation by the mean of the set of data. It is often expressed in parts per thousand (ppt) or in percent by multiplying this ratio by 1000 ppt or by 100%. RSD = s / χ x 1000 ppt The relative standard deviation multiplied by 100% is called the coefficient of variation (CV). CV = s / χ x 100% Relative standard deviations often give a clearer picture of data quality than do absolute standard deviations. Standard Error of the Mean (sm) is the standard deviation of a set of data divided by the square root of the number of data points in the set. s sm  N If a series of replicate results, each containing N measurements, are taken randomly from a population of results, the mean of each set will show less and less scatter as N increases. For example, to increase the precision by a factor of 10 requires 100 times as many measurements. It is better, if possible, to decrease s than to keep averaging more results, since s.; is directly proportional to s, but only inversely proportional to the square root of N. The standard deviation can sometimes be decreased by being more precise in individual operations, by changing the procedure, and by using more precise measurement tools. Example 6-2: Glucose levels are routinely monitored in patients suffering from diabetes. The glucose concentrations in a patient with mildly elevated glucose levels were determined in different months by a spectrophotometric analytical method. The patient was placed on a lowsugar diet to reduce the glucose levels. The following results were obtained during a study to determine the effectiveness of the diet. Calculate a pooled estimate of the standard deviation for the method. Total number of measurements = 24 Total sum of squares = 6907.89 Solution For the first month, the sum of the squares in the next to last column was calculated as follows: Sum of squares = (1108 2 1100.3)2 + (1122 2 1100.3)2 + (1075 2 1100.3)2 + (1099 2 1100.3)2 + (1115 2 1100.3)2 + (1083 2 1100.3)2 + (1100 2 1100.3)2 = 1687.43 The other sums of squares were obtained similarly. The pooled standard deviation is then Note this pooled value is a better estimate of s than any of the individual s values in the last column. Note also that one degree of freedom is lost for each of the four sets. Because 20 degrees of freedom remain, however, the calculated value of s can be considered a good estimate of s. Spread or Range (w): The spread, or range, is another term that is sometimes used to describe the precision of a set of replicate results. It is the difference between the largest value in the set and the smallest. Standard Deviation on of Calculated Results Propagation of Errors A way to keep track of the error in a calculation based on the errors of the variables used in the calculation Example: Suppose you're adding three volumes together and you want to know what the total error (s) is: y = a(±sa) + b(±sb) - c(±sc) + 0.50 (± 0.02) + 4.10 (± 0.03) s2 y = s 2 a + s 2 b + s 2 c - 1.97 (± 0.05) 2.63 and the sum should be reported as 2.64 (±0.06). Example: sy = y X (±0.0289) = 0.0104 x (±0.0289) = ±0.000301 and we can write the answer and its uncertainty as: 0.0104 (±0.0003). Reporting Computed Data A numerical result is worthless unless the user knows something about its accuracy and/or precision. It is always essential to indicate best estimate of the reliability of data. One of the best ways of indicating reliability is to: • give a confidence interval at the 90% or 95% confidence level. • report the absolute standard deviation or the coefficient of variation of the data. It is a good idea to indicate the number of data points that were used to obtain the standard deviation so that the user has some idea of the probable reliability of s. Less satisfactory but more common indicator of the quality of data is the significant figure convention. Significant Figures • Digits in a measurement which are known with certainty, plus a last digit which is estimated beaker graduated cylinder buret Rules for Determining How Many Significant Figures There are in a Number  All nonzero digits are significant (4.006, 12.012, 10.070)  Interior zeros are significant (4.006, 12.012, 10.070)  Trailing (‫ )يعقب‬zeros FOLLOWING a decimal point are significant (10.070)  Trailing zeros PRECEEDING an assumed decimal point may or may not be significant  Leading zeros are not significant. They simply locate the decimal point (0.00002) Reporting the Correct # of Sig Fig’s • Multiplication/Division Rule: Round off to the fewest number of sig figs originally present 12.154 5.23 36462 24308 60770 63.56542 ans = 63.5 Reporting the Correct # of Sig Fig’s  Addition/Subtraction 15.02 9,986.0 3.518 10004.538 Rule: Round off to the least certain decimal place Logarithms and Antilogarithms 1. 2. In a logarithm of a number, keep as many digits to the right of the decimal point as there are significant figures in the original number. In an antilogarithm of a number, keep as many digits as there are digits to the right of the decimal point in the original number. Rounding Off Rules • digit to be dropped > 5, round UP 158.7 = 159 • digit to be dropped < 5, round DOWN 158.4 = 158 • digit to be dropped = 5, round UP if result is EVEN 158.5 = 158 157.5 = 157 Wait until the END of a calculation in order to avoid a “rounding error” ? sig figs 5 sig figs (1.235 - 1.02) x 15.239 = 2.923438 = 1.12 3 sig figs 1.235 -1.02 0.215 = 0.22 Homework Calculate the following: (each # represents xi±si) 1.10 ( 0.10)  0.25 ( 0.020) 2.57 ( 0.35)

Chapter 6 Random Error

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