MCQs Estimation 6

MCQs from Statistical Inference covering the topics of Estimation and Hypothesis Testing 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 Quiz will help the learner to understand the related concepts and enhance the knowledge too.

1. By the method of moments one can estimate:


2. Roa-Blackwell Theorem enables us to obtain minimum variance unbiased estimator through:


3. If $n_1=16$, $n_2=9$ and $\alpha=0.01$, then $t_{\frac{\alpha}{2}}$ will be


4. A quantity obtained by applying a certain rule or formula is known as


5. If the sample average $\overline{x}$ is an estimate of the population mean $\mu$, then $\overline{x}$ is:


6. The consistency of an estimator can be checked by comparing


7. Sample median as an estimator of the population mean is always


8. Criteria to check a point estimator to be good are


9. Crammer-Rao inequality is valid in the case of:


10. An estimator $T_n$ is said to be a sufficient statistic for a parameter function $\tau(\theta)$ if it contained all the information which is contained in the:


11. For an estimator to be consistent, the unbiasedness of the estimator is


12. If $\alpha=0.10$ and $n=15$ then $t_{\frac{\alpha}{2}}$ will be


Most of the MCQs on this page are covered from Estimate and Estimation, Testing of Hypothesis, Parametric and Non-Parametric tests, etc. Let’s start the MCQs Hypothesis Testing quiz now.

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