Q. The parameters of population are denoted by the

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Q. The mistakes or biases which are considered as causes of non-sampling errors must includes

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Q. Regardless to difference in distribution of sample and population, the mean of sampling distribution must be equal to

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Q. The cluster sampling, stratified sampling and systematic sampling are types of

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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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