Probability Distributions and Inferential Testing MCQs
Probability Distributions and Inferential Testing MCQs
Welcome to MCQss.com's collection of multiple-choice questions (MCQs) focused on Probability Distributions and Inferential Testing. This page aims to assess and improve your understanding of key concepts related to probability distributions, random variables, and inferential testing.
Probability distributions form the foundation of statistical analysis, and these MCQs will cover various types of probability distributions, such as the binomial, normal (Gaussian), and exponential distributions. You will encounter questions related to the probability density function (PDF) and cumulative distribution function (CDF) of these distributions, as well as properties and characteristics specific to each distribution.
Understanding probability distributions and inferential testing is vital for making statistical inferences, testing hypotheses, and drawing conclusions from sample data. These MCQs offer an interactive platform to assess your proficiency in these concepts and enhance your analytical skills.
By engaging with these MCQs, you will develop a deeper understanding of probability distributions, random variables, and inferential testing. You will also strengthen your problem-solving abilities, critical thinking skills, and the application of statistical concepts in real-world scenarios.
These MCQs are suitable for students, researchers, and professionals seeking to test their knowledge and improve their understanding of probability distributions and inferential testing. Whether you are studying statistics, conducting data analysis, or using inferential testing in your work, these MCQs provide a valuable resource for self-assessment and learning.
Expand your knowledge of Probability Distributions and Inferential Testing by exploring and answering these MCQs. Test your understanding of key concepts such as probability, random variables, hypothesis testing, and more.
1: Binomial distribution is probability distribution for which there are just two possible outcomes with fixed probabilities that sum to _____
A. Zero
B. 0.1
C. 1.0
D. 10
2: According to the Bounding rule of Probabilities the probability of any event can never be ______
A. Less than zero
B. Greater than zero
C. Equal to zero
D. Both a and b
3: Central Limit Theorem is a statistical theorem that states that the sampling distribution of any statistic will approximate normality as the sample size_____.
A. Increases
B. Decreases
C. Remains same
D. Both a and b
4: Complement of event A is the set of all outcomes of a sample space that are not A. It is calculated as _____
A. 1 – P(A)
B. P- 1(A)
C. P- 0(A)
D. None of these
5: Conditional Probability is the probability of one event occurring (A) given that another event has occurred (B), written as_____
A. P(A/B)
B. P(A-B)
C. P(A+B)
D. P(A=B)
6: Region set by the alpha level to determine the rejection region for a _____ hypothesis test.
A. Simple
B. Complex
C. Directional
D. Null
7: Statistics used to describe the distribution of a sample or population is known as ______
A. Descriptive Statistics
B. Inferential Statistics
C. Exploratory Statistics
D. None of these
8: According to general Addition Rule of Probabilities if two events are not mutually exclusive, the probability of event A occurring or event B occurring is equal to the sum of their separate probabilities _____ their joint probability.
A. Plus
B. Minus
C. Square
D. Square root
9: According to General rule of Multiplication Rule of Probabilities if two events are not independent of each other, the probability of event A occurring and event B occurring is equal to the _____ of the unconditional probability of event A and the conditional probability of event B given A.
A. Product
B. Sum of product
C. Square of product
D. None of these
10: When two events are independent, knowledge of one event helps predict the probability of the other event occurring.
A. True
B. False
11: Statistical tools for estimating how likely it is that a statistical result based on data from a random sample is representative of the population from which the sample has been selected is known as ______
A. Inferential Statistics
B. Descriptive Statistics
C. Exploratory Statistics
D. None of these
12: Mutually Exclusive events are the events that cannot occur at the same time. In other words, there is no intersection of mutually exclusive events so their joint probability is equal to_____.
A. One
B. Zero
C. Hundred
D. Both a and b
13: In a hypothesis test, the null hypothesis is the hypothesis that is initially assumed to be_____.
A. True
B. False
C. Both
D. Neutral
14: Distribution of all possible outcomes of a trial and the associated probability of each outcome is known as Probability Distribution.
A. True
B. False
15: According to Restricted addition Rule of probabilities if two events are mutually exclusive, the probability of event A occurring or event B occurring is equal to the _____ of their separate probabilities.
A. Sum
B. Sum of square
C. Square of sum
D. None of these
16: If two events are independent of each other, the probability of event A occurring and event B occurring is equal to the product of their _____ probabilities is known as Restricted Multiplication Rule of probabilities.
A. Separate
B. Combined
C. Both
D. None
17: Probability distribution of a sample statistic drawn from a very large number of samples from some given population is known as ______
A. Standard Distribution
B. Skewed Distribution
C. Sampling Distribution
D. None of these
18: Score from the standard normal probability distribution that indicates how many standard deviation units a score is from the mean of zero is known as ______
A. Standard Score
B. Sampling score
C. Z score
D. Both a and c
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Probability Distributions and Inferential Testing MCQs
