Statistics for Data Analyst - Statistics MCQs, Analysis, Software

Confidence Interval MCQs 4

Apr 29, 2024Aug 19, 2021 by Muhammad Imdad Ullah

MCQs from Statistical Inference covering the topics of Estimation Confidence Interval MCQs for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities. The Estimation and Confidence interval MCQs will help the learner to understand the related concepts and enhance the knowledge too. Let us start with Confidence Interval MCQs

Most of the MCQs on this page are covered from Estimate and Estimation, Testing of Hypothesis, Parametric and Non-Parametric tests, etc.

Statistical inference is a branch of statistics in which we conclude (make wise decisions) about the population parameter by making use of sample information. Statistical inference can be further divided into the Estimation of parameters and testing of the hypothesis.

Estimation is a way of finding the unknown value of the population parameter from the sample information by using an estimator (a statistical formula) to estimate the parameter. One can estimate the population parameter by using two approaches (I) Point Estimation and (ii) Interval Estimation.

In point Estimation, a single numerical value is computed for each parameter, while in interval estimation a set of values (interval) for the parameter is constructed. The width of the confidence interval depends on the sample size and confidence coefficient. However, it can be decreased by increasing the sample size. The estimator is a formula used to estimate the population parameter by making use of sample information.

Online Confidence Interval MCQs

Estimates given in the form of confidence intervals are called
$(1-\alpha)$ is called
If $(1-\alpha)$ is increased, the width of a confidence interval is
By decreasing the sample size, the confidence interval becomes
The confidence interval becomes narrow by increasing the
The distance between an estimate and the estimated parameter is called
By increasing the sample size, the precision of the confidence interval is _______
The number of values that are free to vary after a certain restriction is applied to the data is called
A 95% confidence interval for the mean of a population is such that A confidence interval will be widened if
A statistician calculates a 95% confidence interval for $\mu$ and $\sigma$ is known.
The confidence interval is RS 18000 to RS 22000, and the amount of the sample mean $\overline{X}$ is
If the population standard deviation $\sigma$ is known, the confidence interval for the population mean $\mu$ is based on
If the population standard deviation $\sigma$ is unknown, and the sample size is small ($n\le 30$), the confidence interval for the population mean $\mu$ is based on
The shape of the t-distribution depends upon the
If the population standard deviation $\sigma$ is doubled, the width of the confidence interval for the population mean $\mu$ (the upper limit of the confidence interval — the lower limit of the confidence interval) will be
A range of values calculated from the sample data and it is likely to contain the true value of the parameter with some probability is called
The estimator is said to be ________ if the mean of the estimator is not equal to the mean of the population parameter.
Estimation can be classified into
A single value used to estimate the value of the population parameter is called
The probability associated with the confidence interval is called

It is important to note that the point estimates are simpler to calculate but lack information about precision. On the other hand, interval estimates provide more information but require more calculations too, and often rely on assumptions about the data. Therefore, the choice between point estimation and interval estimation depends on the specific research question and how much detail a researcher needs about the population parameter being estimated.

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Important MCQ Sampling Quiz – 7

Sep 7, 2024Aug 15, 2021 by Muhammad Imdad Ullah

The Online MCQ sampling Quiz is about the Basics of Sampling and Sampling Distributions. It will help you understand the basic concepts of sampling methods and distributions. This Online MCQ Sampling Quiz will help the students to prepare for different exams related to education or jobs. Most of the MCQs on this page cover MCQ Sampling and Sampling Distributions, Probability Sampling and Non-Probability Sampling, Mean and Standard Deviation of Sample, Sample size, Sampling error, Sample bias, Sample Selection, etc.

Please go to Important MCQ Sampling Quiz – 7 to view the test

Online MCQ Sampling Quiz

If $E(\overline{X})=10$ and $\mu=10$ then bias is equal to
If $\overline{X}=10$ and $\mu=12$ then sampling error is
The standard deviation of the distribution of sample means is equal to
If $n=25$, $\sigma^2=25$, and $\overline{X}=25$, then standard error of $\overline{X}$ will be
$S^2=\frac{\sum (X-\overline{X})^2}{n}$ is called
$s^2=\frac{\sum(X-\overline{X})^2}{n-1}$ is called
If $E(s^2)=3$ and $\sigma^2=2$ then bias will be
In sampling without replacement, the standard error of sampling distribution of sample proportion $\hat{p}$ is equal to
When sampling is done without replacement $\sigma_{\overline{X}}$ is equal to
In case of sampling with replacement $\sigma_{\hat{p}1 – \hat{p}_2}$ is equal to
The distribution of the means of samples of size 4, taken from a population with a standard deviation $\sigma$, has a standard deviation of
In sampling with replacement, $\sigma{\overline{X}_1 – \overline{X}_2}$ is equal to
When sampling is done with or without replacement, $E(\hat{p}_1 – \hat{p}_2)$ is equal to
In case of sampling with replacement, $E(S^2)$ is equal to
In sampling without replacement, the expected value of $S^2$ is equal to
When sampling is done with replacement, then $\mu{s^2}$ is equal to
In sampling without replacement, $\mu_{s^2}$ is equal to
When sampling is done with or without replacement, $\mu_{\overline{X}_1-\overline{X}_2}$ is equal to
If $X$ represents the number of units having the specified characteristic and $n$ is the size of the sample then sample proportion $\hat{p}$ is equal to
If $X$ represents the number of units having the specified characteristic and $N$ is the size of the population, then population proportion $p$ is equal to

MCQs Sampling Quiz and Sampling Distribution

MCQ sampling Quiz for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities.

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R and Data Analysis

Important Quiz Sampling Distribution – 6

May 13, 2024Aug 6, 2021 by Muhammad Imdad Ullah

The Quiz Sampling Distribution is about the Basics of Sampling and Sampling Distributions. It will help you understand the basic concepts of sampling methods and distributions. This test will also help you prepare for different exams related to education or jobs. Most of the MCQs on this page cover Quiz Sampling Distribution, Probability Sampling and Non-Probability Sampling, Mean and Standard Deviation of Sample, Sample size, Sampling error, Sample bias, and Sample Selection.

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Online Quiz Sampling Distribution

If $\sigma_1=\sigma_2=\sigma$ and $n_1\ne n_2$, then S.E. ($\overline{X}2-\overline{X}_1$) is
In sampling with replacement, the standard error of the sample proportion $\hat{p}$ is equal to
If $p_1=p_2=p$ and $n_1\ne n_2$, then S.E ($\hat{p}_1 – \hat{p}_2)$ is
The selection of the cricket team for the World Cup is called
A complete list of all the sampling units is called:
A plan for obtaining a sample from a population is called
If a survey is conducted by a sampling design is called
The difference between the expected value of a statistic and the value of the parameter being estimated is called a
The standard deviation of any sampling distribution is called
The standard error increases when the sample size is ___________
The mean of sampling distribution of means is equal to
The mean of sampling distribution of means is equal to
$\frac{\text{Sum of all sample means} }{\text{Total number of samples}}$ is equal to
A sample which is free from bias is called
If $E(\overline{X})=\mu$ then bias is
The weight of stratum $i$ is equal to the proportion of
The value of $n_1$ by a proportional allocation from the following information is $N_1=580$, $N_2=140$, and $n=80$.
Which of the following is not a type of non-probability sampling?
If we have a population of 5, 4, 6, 8, and 9 and a sample size is 2, how many possible samples will be there?
A procedure in which the number of elements in a stratum is not proportional to the number of elements in the population is classified as

MCQs Quiz sampling distribution for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities.

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Important Sampling and Sampling Distribution MCQs – 5

May 1, 2024Aug 4, 2021 by Muhammad Imdad Ullah

The Online Sampling and Sampling Distribution MCQs for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities. Most of the questions in this quiz Sampling and Sampling Distribution MCQs cover the topics of Probability Sampling and Non-Probability Sampling, Mean and Standard Deviation of Sample, Sample size, Sampling error, Sample bias, Sample Selection, etc.

Please go to Important Sampling and Sampling Distribution MCQs – 5 to view the test

The sampling Quiz is about the Basics of Sampling and Sampling Distribution MCQs. It will help you understand the basic concepts of sampling methods and distributions. This test will also help you prepare for different exams related to education or jobs.

Sampling and Sampling Distribution MCQs

In sampling without replacement, an element can be chosen
In sampling with replacement, the following is always true _________
Suppose a finite population has 6 items and 2 items are selected at random without replacement, then all possible samples will be
Suppose a finite population contains 7 items and 3 items are selected at random without replacement, then all possible samples will be
A population contains $N$ items and all possible samples of size $n$ are selected without replacement. The possible number of samples will be
Suppose a finite population contains 4 items and 2 items are selected at random with replacement, then how many samples will be there
A population contains 2 items and 4 items are selected at random with replacement, then all possible samples will be
Suppose a population has $N$ items and $n$ items are selected with replacement. The number of all possible samples will be
In random sampling, the probability of selecting an item from the population is _________.
Random Sampling is also called ___________.
Non-random sampling is also called
Sampling error is reduced by
If $N$ is the size of the population and $n$ is the sample size, then the sampling fraction is ________
The finite population correction factor is ____________
In sampling with replacement, the standard error of $\overline{X}$ is equal to
What concept states that the sampling distribution of the mean approaches a normal distribution as the sample size increases?
Which of the following are examples of sampling bias? Select all that apply.
A plan for obtaining a sample from a population is called
In sampling without replacement, an element can be chosen
Which one of the following is the main problem with using non-probability sampling techniques?

The important points about Sampling and Sampling Distributions are:

The shape of the sampling distribution depends on the underlying population distribution, the statistic being calculated (mean, median, etc.), and the sample size.
Larger sample sizes tend to produce sampling distributions that are more normally distributed (bell-shaped and symetrical), regardless of the population distribution due to the Central Limit Theorem.
The sampling distribution is used to make inferences about the population from which the sample was drawn. For example, we can estimate the population mean by looking at the average of the sample means from many samples.

By understanding the concepts and theories about sampling and sampling distributions, one can make informed decisions based on the data collected from samples, even though one can not easily study the entire population.