What is the statistic called whose specific value is used to estimate a population parameter?
Correct answer A. Point estimator
Topic / 67 questions
Statistical Inference MCQs
Statistical Inference MCQs are multiple-choice questions on drawing conclusions about a population from sample data, covering estimation, confidence intervals and hypothesis testing. They help statistics students and candidates for PPSC, FPSC, lecturer tests and UGC NET. Every question shows its correct answer instantly, so you can check concepts such as bias, sufficiency and significance levels quickly.
Because E(x̄) = ____, x̄ is said to be an unbiased estimator of the population mean. Which symbol fills the blank?
Correct answer B. μ
Sampling error decreases when the sample size is?
Correct answer B. Increased
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What is a particular value of a sample statistic called?
Correct answer D. Estimate
Estimation and hypothesis testing are the two main branches of?
Correct answer C. Statistical Inference
What are the 95% confidence limits for a population proportion P?
Correct answer A. p ± 1.96√(pq/n)
What is the hypothesis that is actually being tested called?
Correct answer B. Null hypothesis
A Type II error is committed when the null hypothesis is?
Correct answer B. Accepted when it is false
Which of the following cannot be a null hypothesis H0?
Correct answer A. θ ≠ θ0
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If the null hypothesis is H₀: θ = θ₀, how is the general (two-sided) alternative hypothesis stated?
Correct answer D. H₁: θ ≠ θ₀
What is the first step in testing a hypothesis?
Correct answer D. Stating H0
Which criteria are used to judge whether a point estimator is good?
Correct answer D. All of the above
The consistency of an estimator can be checked by comparing its?
Correct answer B. Variance
Herbicide A has long been used to kill a certain weed. An experiment will test whether a new Herbicide B is more effective; A stays in use unless there is enough evidence that B is better. What is the alternative hypothesis?
Correct answer B. Herbicide B is more effective than Herbicide A
Which assumption underlies the use of the t-distribution?
Correct answer B. The samples are drawn from a normally distributed population
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What is the shape of the t-distribution?
Correct answer C. Symmetrical
Which statement correctly compares the t-distribution with the standard normal distribution?
Correct answer A. The greater the degrees of freedom, the more the t-distribution resembles the standard normal distribution
When is the Mann-Whitney U test preferred to a t-test?
Correct answer A. The assumption of normality is not met
What does the sign test assume about the samples?
Correct answer B. Samples are dependent
Six people each rate three brands of coffee for taste on a scale of 1 to 10, giving 18 observations. Which test is appropriate for checking whether the brands taste equally good?
Correct answer C. Kruskal-Wallis test
About Statistical Inference MCQs
Estimation forms the first part of this category. Questions cover point estimators and estimates, unbiased and biased estimators defined through expected values, sufficient statistics based on conditional distributions, and confidence limits for a population proportion. You will need to recognise what a sample mean estimates and interpret the meaning of 1 minus alpha as the confidence coefficient. Questions are often written in symbols, so reading notation fluently saves time.
Hypothesis testing is the second strand. Items ask about critical values that separate the rejection and acceptance regions, how those values come from the sampling distribution of the test statistic, the level of significance denoted by alpha and the risk of Type I error when the null hypothesis is rejected. These ideas are core to statistics lecturer and statistical officer recruitment papers.
Key facts to remember
- An estimator is unbiased if its expected value equals the parameter it estimates.
- The level of significance, denoted by alpha, is the probability of a Type I error.
- A Type I error occurs when a true null hypothesis is rejected.
- Critical values separate the rejection region from the acceptance region of a test.
- A statistic is sufficient if the sample's conditional distribution given it does not depend on the parameter.
Exams that include Statistical Inference MCQs
Pakistan
- PPSC
- FPSC
- SPSC
- KPPSC
- Lecturer tests
- NTS
India
- UGC NET
- State PSC exams
- Campus placements
Bangladesh
- BCS
- Government job tests
- University admission tests (DU, RU, JU)
How to prepare for Statistical Inference MCQs
- Write the definitions of unbiasedness, consistency, efficiency and sufficiency side by side, because options often swap these properties.
- Draw a two-by-two table of decisions and reality to remember when Type I and Type II errors occur.
- Memorise common z values such as 1.645, 1.96 and 2.576, which are needed for confidence interval questions.
- Practise reading short notation like E(θ̂) = θ and translate it into words before choosing the matching property.
Frequently asked questions
What topics are covered in Statistical Inference MCQs?
Statistical Inference MCQs cover point and interval estimation, properties of estimators such as unbiasedness and sufficiency, confidence intervals for means and proportions, null and alternative hypotheses, test statistics, critical regions, significance levels and Type I and Type II errors.
How many Statistical Inference MCQs are on MCQs360?
There are 73 Statistical Inference MCQs on MCQs360, each with an instant answer. Practice is free and needs no sign-up, so you can study inference alongside the probability category to build a complete statistics foundation.
Which exams ask Statistical Inference MCQs?
These questions appear in statistics lecturer tests and statistical officer posts conducted by PPSC, FPSC, SPSC and KPPSC in Pakistan. In India they are part of UGC NET papers that include statistics and of State PSC statistical services. In Bangladesh they feature in BCS statistics cadre and related government tests.
What is the difference between a Type I and a Type II error?
A Type I error happens when you reject a null hypothesis that is actually true, and its probability is the significance level alpha. A Type II error happens when you fail to reject a null hypothesis that is actually false, and its probability is called beta. Reducing one usually increases the other unless the sample size grows.
Last reviewed October 2026