statistics type i amp ii errors

Sampling methods are the ways to choose people from the population to be considered in a sample survey. Samples can be divided based on following criteria.

  • Probability samples – In such samples, each population element has a known probability or chance of being chosen for the sample.
  • Non-probability samples – In such samples, one can not be assured of having known probility of each population element.

Probability sampling methods

Probability sampling methods ensures that the sample choosen represent the population correctly and the survey conducted will be statistically valid. Following are the types of probability sampling methods:

  • Simple random sampling. – This method refers to a method having following properties:
    • The population have N objects.
    • The sample have n objects.
    • All possible samples of n objects have equal probability of occurence.

    One example of simple random sampling is lottery method. Assign each population element a unique number and place the numbers in bowl.Mix the numbers throughly. A blind-folded researcher is to select n numbers. Include those population element in the sample whose number has been selected.

  • Stratified sampling – In this type of sampling method, population is divided into groups called strata based on certain common characteristic like geography. Then samples are selected from each group using simple random sampling method and then survey is conducted on people of those samples.
  • Cluster sampling – In this type of sampling method, each population member is assigned to a unique group called cluster. A sample cluster is selected using simple random sampling method and then survey is conducted on people of that sample cluster.
  • Multistage sampling – In such case, combination of different sampling methods at different stages. For example, at first stage, cluster sampling can be used to choose clusters from population and then sample random sampling can be used to choose elements from each cluster for the final sample.
  • Systematic random sampling – In this type of sampling method, a list of every member of population is created and then first sample element is randomly selected from first k elements. Thereafter, every kth element is selected from the list.

Non-probability sampling methods

Non-probability sampling methods are convenient and cost-savvy. But they do not allow to estimate the extent to which sample statistics are likely to vary from population parameters. Whereas probability sampling methods allows that kind of analysis. Following are the types of non-probability sampling methods:

  • Voluntary sample – In such sampling methods, interested people are asked to get involved in a voluntary survey. A good example of voluntary sample in on-line poll of a news show where viewers are asked to participate. In voluntary sample, viewers choose the sample, not the one who conducts survey.
  • Convenience sample – In such sampling methods, surveyor picks people who are easily available to give their inputs. For example, a surveyer chooses a cinema hall to survey movie viewers. If the cinema hall was selected on the basis that it was easier to reach then it is a convenience sampling method.

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

9.statistics beta distribution

10.statistics binomial distribution

11.statistics blackscholes model

12.statistics boxplots

13.statistics central limit theorem

14.statistics chebyshevs theorem

15.statistics chisquared distribution

16.statistics chi squared table

17.statistics circular permutation

18.statistics cluster sampling

19.statistics cohens kappa coefficient

20.statistics combination

21.statistics combination with replacement

22.statistics comparing plots

23.statistics continuous uniform distribution

24.statistics cumulative frequency

25.statistics coefficient of variation

26.statistics correlation coefficient

27.statistics cumulative plots

28.statistics cumulative poisson distribution

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

70.statistics permutation

71.statistics permutation with replacement

72.statistics pie chart

73.statistics poisson distribution

74.statistics pooled variance r

75.statistics power calculator

76.statistics probability

77.statistics probability additive theorem

78.statistics probability multiplicative theorem

79.statistics probability bayes theorem

80.statistics probability density function

81.statistics process capability cp amp process performance pp

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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