Đề 6 – Bài tập, đề thi trắc nghiệm online Lý thuyết xác suất và thống kê

A A. To determine causality between variables. B B. To measure the strength and direction of a linear relationship between two quantitative variables. C C. To compare means of different groups. D D. To predict future values of a variable with certainty.

A A. To make data analysis more complicated. B B. To replace statistical analysis. C C. To communicate data patterns, insights, and findings effectively and understandably. D D. To hide outliers in data.

A A. Descriptive statistics are used for populations, while inferential statistics are used for samples. B B. Descriptive statistics summarize and describe data, while inferential statistics use sample data to make generalizations or inferences about a population. C C. Descriptive statistics are more complex than inferential statistics. D D. There is no difference; they are the same thing.

A A. To estimate the probability of a specific event occurring. B B. To provide a range of plausible values for a population parameter based on sample data. C C. To determine the exact value of a population parameter. D D. To test the hypothesis that the sample mean is equal to the population mean.

A A. Chi-squared test B B. Correlation test C C. T-test D D. ANOVA

A A. The probability that the null hypothesis is true. B B. The probability of rejecting the null hypothesis when it is actually true. C C. The probability of observing the data (or more extreme data) if the null hypothesis were true. D D. The probability of accepting the null hypothesis when it is false.

A A. The central tendency of a dataset. B B. The average squared deviation from the mean, indicating the spread of data points around the mean. C C. The frequency of the most common value in a dataset. D D. The difference between the maximum and minimum values in a dataset.

A A. 0 B B. 0.5 C C. 1 D D. Infinity

A A. Variance is the square root of the standard deviation. B B. Standard deviation is the square root of the variance. C C. They measure different aspects of data dispersion. D D. They are the same thing, just different terms.

A A. Failing to reject a false null hypothesis. B B. Correctly rejecting a false null hypothesis. C C. Rejecting a true null hypothesis. D D. Correctly failing to reject a true null hypothesis.

A A. To make the experiment more complex. B B. To introduce bias into the study. C C. To ensure that treatment groups are as similar as possible at the start of the experiment, reducing the influence of confounding variables. D D. To make data analysis easier.

A A. Height in centimeters B B. Temperature in degrees Celsius C C. Eye color D D. Weight in kilograms

A A. Symmetric around the mean. B B. Unimodal (has one mode). C C. Skewed to the right. D D. Bell-shaped.

A A. As sample size increases, margin of error increases. B B. As sample size decreases, margin of error decreases. C C. As sample size increases, margin of error decreases. D D. Sample size and margin of error are unrelated.

A A. Rolling a fair die and getting a 6. B B. Drawing a red card from a standard deck of cards. C C. The probability of rain tomorrow, given that it is cloudy today. D D. The probability of winning the lottery.

A A. To predict future outcomes based on past data. B B. To reduce the dimensionality of data by identifying underlying factors that explain the correlations among a set of observed variables. C C. To compare means of different groups. D D. To test hypotheses about population parameters.

A A. A population is always larger than a sample. B B. A population is the entire group of individuals we are interested in studying, while a sample is a subset of the population. C C. A sample is more reliable than a population for statistical analysis. D D. There is no difference; the terms are interchangeable.

A A. It states that the probability of any event is between 0 and 1. B B. It describes the distribution of sample means approaching a normal distribution as the sample size increases, regardless of the population distribution. C C. It is used to calculate the probability of rare events. D D. It defines the relationship between variance and standard deviation.

A A. To reduce the sample size. B B. To estimate the sampling distribution of a statistic and assess its variability when analytical solutions are complex or unavailable. C C. To make data analysis faster. D D. To replace traditional statistical methods.

A A. Probability deals with past events, while statistics deals with future events. B B. Probability predicts the likelihood of future events based on known parameters, while statistics infers parameters from observed data. C C. Probability is used for small datasets, while statistics is used for large datasets. D D. Probability is a branch of mathematics, while statistics is a branch of computer science.

A A. The probability calculated after observing the data. B B. The probability of the data given the hypothesis. C C. The initial belief or probability of a hypothesis before observing any data. D D. The probability of rejecting the null hypothesis.

A A. Large sample size B B. Random sampling C C. Non-response bias D D. Double-blind design

A A. Simple random sampling B B. Stratified sampling C C. Cluster sampling D D. Convenience sampling

A A. Normal distribution B B. Poisson distribution C C. Binomial distribution D D. Exponential distribution

A A. Discrete variables can take any value, while continuous variables can only take integer values. B B. Discrete variables can only take integer values or countable values, while continuous variables can take any value within a range. C C. Discrete variables are always positive, while continuous variables can be negative. D D. There is no difference between them.

A A. The practical importance of the results. B B. The certainty that the results are absolutely true. C C. The likelihood that the observed effect is not due to random chance alone, based on a chosen significance level. D D. The size of the sample used in the study.

A A. Variance B B. Standard Deviation C C. Median D D. Range

A A. It is always difficult to calculate. B B. It is not affected by extreme values (outliers). C C. It is sensitive to extreme values (outliers). D D. It cannot be used with continuous data.

A A. The number of data points in a dataset. B B. The number of independent pieces of information available to estimate a parameter. C C. The level of confidence in a statistical test. D D. The probability of making a Type I error.

A A. To summarize data using descriptive statistics. B B. To test hypotheses about population parameters. C C. To model the relationship between a dependent variable and one or more independent variables. D D. To calculate probabilities of events.