statistics arithmetic median

The standard deviation of a sampling distribution is called as standard error. In sampling, the three most important characteristics are: accuracy, bias and precision. It can be said that:

  • The estimate derived from any one sample is accurate to the extent that it differs from the population parameter. Since the population parameters can only be determined by a sample survey, hence they are generally unknown and the actual difference between the sample estimate and population parameter cannot be measured.
  • The estimator is unbiased if the mean of the estimates derived from all the possible samples equals the population parameter.
  • Even if the estimator is unbiased an individual sample is most likely going to yield inaccurate estimate and as stated earlier, inaccuracy cannot be measured. However it is possible to measure the precision i.e. the range between which the true value of the population parameter is expected to lie, using the concept of standard error.


$SE_\bar{x} = \frac{s}{\sqrt{n}}$

Where −

  • ${s}$ = Standard Deviation
  • and ${n}$ = No.of observations


Problem Statement:

Calculate Standard Error for the following individual data:

Items 14 36 45 70 105


Let’s first compute the Arithmetic Mean $\bar{x}$

$\bar{x} = \frac{14 + 36 + 45 + 70 + 105}{5} \\[7pt]
\, = \frac{270}{5} \\[7pt]
\, = {54}$

Let’s now compute the Standard Deviation ${s}$

$s = \sqrt{\frac{1}{n-1}((x_{1}-\bar{x})^{2}+(x_{2}-\bar{x})^{2}+…+(x_{n}-\bar{x})^{2})} \\[7pt]
\, = \sqrt{\frac{1}{5-1}((14-54)^{2}+(36-54)^{2}+(45-54)^{2}+(70-54)^{2}+(105-54)^{2})} \\[7pt]
\, = \sqrt{\frac{1}{4}(1600+324+81+256+2601)} \\[7pt]
\, = {34.86}$

Thus the Standard Error $SE_\bar{x}$

$SE_\bar{x} = \frac{s}{\sqrt{n}} \\[7pt]
\, = \frac{34.86}{\sqrt{5}} \\[7pt]
\, = \frac{34.86}{2.23} \\[7pt]
\, = {15.63}$

The Standard Error of the given numbers is 15.63.

The smaller the proportion of the population that is sampled the less is the effect of this multiplier because then the finite multiplier will be close to one and will affect the standard error negligibly. Hence if the sample size is less than 5% of population, the finite multiplier is ignored.

Table of Contents
1.statistics adjusted rsquared

2.statistics analysis of variance

3.statistics arithmetic mean

4.statistics arithmetic median

5.statistics arithmetic mode

6.statistics arithmetic range

7.statistics bar graph

8.statistics best point estimation

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13.statistics central limit theorem

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15.statistics chisquared distribution

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23.statistics continuous uniform distribution

24.statistics cumulative frequency

25.statistics coefficient of variation

26.statistics correlation coefficient

27.statistics cumulative plots

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29.statistics data collection

30.statistics data collection questionaire designing

31.statistics data collection observation

32.statistics data collection case study method

33.statistics data patterns

34.statistics deciles statistics

35.statistics dot plot

36.statistics exponential distribution

37.statistics f distribution

38.statistics f test table

39.statistics factorial

40.statistics frequency distribution

41.statistics gamma distribution

42.statistics geometric mean

43.statistics geometric probability distribution

44.statistics goodness of fit

45.statistics grand mean

46.statistics gumbel distribution

47.statistics harmonic mean

48.statistics harmonic number

49.statistics harmonic resonance frequency

50.statistics histograms

51.statistics hypergeometric distribution

52.statistics hypothesis testing

53.statistics interval estimation

54.statistics inverse gamma distribution

55.statistics kolmogorov smirnov test

56.statistics kurtosis

57.statistics laplace distribution

58.statistics linear regression

59.statistics log gamma distribution

60.statistics logistic regression

61.statistics mcnemar test

62.statistics mean deviation

63.statistics means difference

64.statistics multinomial distribution

65.statistics negative binomial distribution

66.statistics normal distribution

67.statistics odd and even permutation

68.statistics one proportion z test

69.statistics outlier function

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72.statistics pie chart

73.statistics poisson distribution

74.statistics pooled variance r

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78.statistics probability multiplicative theorem

79.statistics probability bayes theorem

80.statistics probability density function

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82.statistics process sigma

83.statistics quadratic regression equation

84.statistics qualitative data vs quantitative data

85.statistics quartile deviation

86.statistics range rule of thumb

87.statistics rayleigh distribution

88.statistics regression intercept confidence interval

89.statistics relative standard deviation

90.statistics reliability coefficient

91.statistics required sample size

92.statistics residual analysis

93.statistics residual sum of squares

94.statistics root mean square

95.statistics sample planning

96.statistics sampling methods

97.statistics scatterplots

98.statistics shannon wiener diversity index

99.statistics signal to noise ratio

100.statistics simple random sampling

101.statistics skewness

102.statistics standard deviation

103.statistics standard error se

104.statistics standard normal table

105.statistics statistical significance

106.statistics formulas

107.statistics notations

108.statistics stem and leaf plot

109.statistics stratified sampling

110.statistics student t test

111.statistics sum of square

112.statistics tdistribution table

113.statistics ti 83 exponential regression

114.statistics transformations

115.statistics trimmed mean

116.statistics type i amp ii errors

117.statistics variance

118.statistics venn diagram

119.statistics weak law of large numbers

120.statistics z table

121.discuss statistics

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