Q. The elements in sample with specific characteristics is divided to sample size to calculate

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Q. In sampling distribution, the standard deviation must be equal to

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Q. The method of sampling in which the random sampling will not be possible because the population is widely spread is classified as

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Q. The procedure in which the number of elements in stratum are not in proportional to the number of elements in population is classified as

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Q. The sample statistics are denoted by the

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

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Q. The value of estimator is subtracted from mean and then divided by standard deviation to calculate

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Q. If the proportion of population is 10.5 then the proportion mean of sampling distribution is

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Q. The bias which occurs when the randomly drawn sample from population fails to represent whole population is classified as

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Q. In sample distribution, the degree of freedom is calculated as

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Q. If the p is equal to 0.65, the value of N is 25000 whereas the sample size is 50 then the value of standard deviation of sample proportion is

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Q. The type of sampling In which each element of population has equally likely chance of occurrence in a random sample is classified as

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Q. The type of sampling in which the desired and useful information is gathered from the best position holder is classified as

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Q. When the statistical inference is made on the basis of sample results about the characteristics of population then this is classified as

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Q. The distribution of difference of proportions approximate the normal standard distribution only if

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Q. The distribution which consists of all the values of sample statistic of sampling is classified as

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Q. The important principles to determine the valid statistical inference must includes

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Q. The difference between corresponding population and unbiased estimate in the terms of absolute value is classified as

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Q. In stratified sampling, the sample drawn randomly from the strata is classified as

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Q. In sampling distribution, the formula of calculating standard deviation of sample proportion is as

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