Q. The bias occurred in collection of sample because of confusing questions in the questionnaire is classified as

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Q. The bias in which few respondents responds to offered questionnaire is classified as

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Q. The principle which states that larger the sample size larger the accuracy and stability is part of

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Q. The unknown or exact value that represents the whole population is classified as

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Q. The listing of elements in the population with the identifiable number is classified as

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Q. In statistical analysis, the sample size is considered large if

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Q. If the standard deviation of the population is known then the μ must be equal to

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Q. The methods in statistics that uses sample statistics to estimate the parameters of the population are considered as

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Q. In systematic sampling, the population is 200 and the selected sample size is 50 then the sampling interval is

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Q. In cluster sampling, the elements of selected clusters are classified as

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Q. The method of sampling in which the population is divided in to mutual exclusive groups that have useful context in statistical research is classified as

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Q. If the mean of population is 25 then the mean of sampling distribution is

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Q. If the population parameter μ and the unbiased estimate of population is x̅ then the sampling error is as

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Q. The theorem which states that as the sample size increases the sampling distribution must approach the normal distribution is classified as

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Q. The conditions such as large sample size to represent population and samples must be drawn randomly are included in

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Q. The measures in sampling that are results of sample analyses are called

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Q. All the values in sample distribution that can freely varies in the selected random sample from population are indicated as

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Q. IF the population standard deviation is not known then the formula used to calculate standard error is as

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Q. If the value of x-bar is 70 and the μ of sampling distribution is 15 with the standard deviation is 20 then the standard normal variable is

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Q. The uncertainty of elements can be reduced with the estimation of

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