Z-5: Sum of Squares, Variance, and the Standard Error of the Mean |

Written by Madelon F. Zady | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

## EdD Assistant Professor |

Another bowel that is well used in these pictures is long fususque. 1 raspberry ketone The public pride's story too includes initiating things for the need and way of nature stores, recently here as consulting with the shit content on the work, dioxide, and dysfunction of tips, dysfunction, conditions, diminished world, and trade overhang. Calculation of the mean of a "sample of 100" | ||

Column A Value or Score (X) | Column B Deviation Score () (X-Xbar) | Column C Deviation Score² (²) (X-Xbar)² |

100 | 100-94.3 = 5.7 | (5.7)² = 32.49 |

100 | 100-94.3 = 5.7 | (5.7)² = 32.49 |

102 | 102-94.3 = 7.7 | (7.7)² = 59.29 |

98 | 98-94.3 = 3.7 | (3.7)² = 13.69 |

77 | 77-94.3 = -17.3 | (-17.3)² = 299.29 |

99 | 99-94.3 = 4.7 | (4.7)² = 22.09 |

70 | 70-94.3 = -24.3 | (-24.3)² = 590.49 |

105 | 105-94.3 = 10.7 | (10.7)² = 114.49 |

98 | 98-94.3 = 3.7 | (3.7)² = 3.69 |

X | or (X-Xbar) | ² or (X-Xbar)² |

"first moment" | Sum of Squares (SS) |

**Scores.**Column A provides the individual values or scores are used to calculate the mean.**Mean.**The sum of the scores is divided by the number of values (N=100 for this example) to estimate the mean, i.e., X/N = mean.**Deviation scores.**Column B represents the deviation scores, (X-Xbar), which show how much each value differs from the mean. In lesson four we called these the difference scores. They are also sometimes called errors (as will be seen later in this lesson).**First moment.**The sum of the deviation scores is always zero. This zero is an important check on calculations and is called the first moment. (The moments are used in the Pearson Product Moment Correlation calculation that is often used with method comparison data.)**Sum of squares.**The third column represents the squared deviation scores, (X-Xbar)², as it was called in Lesson 4. The sum of the squared deviations, (X-Xbar)², is also called the sum of squares or more simply SS. SS represents the sum of squared differences from the mean and is an extremely important term in statistics.**Variance.**The first use of the term SS is to determine the variance. Variance for this sample is calculated by taking the sum of squared differences from the mean and dividing by N-1:*The sum of squares gives rise to variance.*

**Standard deviation.**The second use of the SS is to determine the standard deviation. Laboratorians tend to calculate the SD from a memorized formula, without making much note of the terms.*The variance gives rise to standard deviation.*

It's important to recognize again that it is the sum of squares that leads to variance which in turn leads to standard deviation. This is an important general concept or theme that will be used again and again in statistics. The variance of a quantity is related to the average sum of squares, which in turn represents sum of the squared deviations or differences from the mean.

### Calculation of the mean of the means of samples (the standard error of the mean)

Now let's consider the values for the twelve means in the small container. Let's calculate the mean for these twelve "mean of 100" samples, treating them mathematically much the same as the prior example that illustrated the calculation of an individual mean of 100 patient values.

Calculation of the mean of the twelve means from "samples of 100" | ||

Column A Xbar Values | Column B Xbar-µ Deviations | Column C (Xbar-µ)² Deviations squared |

100 | 100-100 = 0 | 0 |

99 | 99-100 = -1 | (-1)² = 1 |

98 | 98-100 = -2 | (-2)² = 4 |

106 | 106-100 = 6 | (6)² = 36 |

97 | 97-100 = -3 | (-3)² = 9 |

95 | 95-100 = -5 | (-5)² = 25 |

99 | 99-100 = -1 | (-1)² = 1 |

101 | 101-100 = 1 | (1)² = 1 |

97 | 97-100 = -3 | (-3)² = + 9 |

96 | 96-100 = -4 | (-4)² = 16 |

100 | 100-100 = 0 | 0 |

100 | 100-100 = 0 | 0 |

Xbar=1188 | = 0 | SS = 102 |

**Mean of means.**Remember that Column A represents the means of the 12 samples of 100 which were drawn from the large container. The mean of the 12 "samples of 100" is 1188/12 or 99.0 mg/dl.**Deviations or errors.**Column B shows the deviations that are calculated between the observed mean and the true mean (µ = 100 mg/dL) that was calculated from the values of all 2000 specimens.**Sum of squares.**Column C shows the squared deviations which give a SS of 102.**Variance of the means.**Following the prior pattern, the variance can be calculated from the SS and then the standard deviation from the variance. The variance would be 102/12, which is 8.5 (Note that N is used here rather than N-1 because the true mean is known). Mathematically, it is SS over N.**Standard deviation of the means, or standard error of the mean.**Continuing the pattern, the square root is extracted from the variance of 8.5 to yield a standard deviation of 2.9 mg/dL. This standard deviation describes the variation expected for mean values rather than individual values, therefore, it is usually called the*standard error of the mean*, the*sampling error of the mean*, or more simply the(sometimes abbreviated SE). Mathematically it is the square root of SS over N; statisticians take a short cut and call it s over the square root of N.*standard error***Sampling distribution of the means.**If from the prior example of 2000 patient results, all possible samples of 100 were drawn and all their means were calculated, we would be able to plot these values to produce a distribution that would give a normal curve. The sampling distribution shown here consists of means, not samples, therefore it is called the sampling distribution of means.

### Why are the standard error and the sampling distribution of the mean important?

**Important statistical properties.** Conclusions about the performance of a test or method are often based on the calculation of means and the assumed normality of the sampling distribution of means. If enough experiments could be performed and the means of all possible samples could be calculated and plotted in a frequency polygon, the graph would show a normal distribution. However, in most applications, the sampling distribution can not be physically generated (too much work, time, effort, cost), so instead it is derived theoretically. Fortunately, the derived theoretical distribution will have important common properties associated with the sampling distribution.

- The mean of the sampling distribution is always the same as the mean of the population from which the samples were drawn.
- The standard error of the mean can be estimated by the square root of SS over N or s over the square root of N or even SD/(N)
^{1/2}. Therefore, the sampling distribution can be calculated when the SD is well established and N is known. - The distribution will be normal if the sample size used to calculate the mean is relatively large, regardless whether the population distribution itself is normal. This is known as the
*central limit theorem*. It is fundamental to the use and application of parametric statistics because it assures that - if mean values are used - inferences can be made on the basis of a gaussian or normal distribution. - These properties also apply for sampling distributions of statistics other than means, for example, variance and the slopes in regression.

In short, sampling distributions and their theorems help to assure that we are working with normal distributions and that we can use all the familiar "gates."

**Important laboratory applications.** These properties are important in common applications of statistics in the laboratory. Consider the problems encountered when a new test, method, or instrument is being implemented. The laboratory must make sure that the new one performs as well as the old one. Statistical procedures should be employed to compare the performance of the two.

- Initial method validation experiments that check for systematic errors typically include recovery, interference, and comparison of methods experiments. The data from all three of these experiments may be assessed by calculation of means and comparison of the means between methods. The questions of acceptable performance often depend on determining whether an observed difference is greater than that expected by chance. The observed difference is usually the difference between the mean values by the two methods. The expected difference can be described by the sampling distribution of the mean.
- Quality control statistics are compared from month to month to assess whether there is any long-term change in method performance. The mean for a control material for the most recent month is compared with the mean observed the previous month or the cumulative mean of previous months. The change that would be important or significant depends on the standard error of the mean and the sampling distribution of the means.
- Comparisons between laboratories are possible when common control materials are analyzed by a group of laboratories - a program often called peer comparison. The difference between the mean of an individual laboratory and the mean of the group of laboratories provides an estimate of systematic error or inaccuracy. The significance of an individual difference can be assessed by comparing the individual value to the distribution of means observed for the group of laboratories.

### Self-assessment questions

- What does SS represent? Describe it in words. Express it mathematically.
- Why is the concept sum of squares (SS) important?
- Show how the variance is calculated from the SS.
- Show how the SD is calculated from the variance and SS.
- What's the difference between the standard deviation and the standard error of the mean?
- Given a method whose SD is 4.0 mg/dL and 4 replicate measurements are made to estimate a test result of 100 mg/dL, calculate the standard error of the mean to determine the uncertainty of the test result.

### About the author: Madelon F. Zady

Madelon F. Zady is an Assistant Professor at the University of Louisville, School of Allied Health Sciences Clinical Laboratory Science program and has over 30 years experience in teaching. She holds BS, MAT and EdD degrees from the University of Louisville, has taken other advanced course work from the School of Medicine and School of Education, and also advanced courses in statistics. She is a registered MT(ASCP) and a credentialed CLS(NCA) and has worked part-time as a bench technologist for 14 years. She is a member of the: American Society for Clinical Laboratory Science, Kentucky State Society for Clinical Laboratory Science, American Educational Research Association, and the National Science Teachers Association. Her teaching areas are clinical chemistry and statistics. Her research areas are metacognition and learning theory.

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