#1
Which of the following best defines statistical sampling?
The process of collecting data from every individual in a population.
The process of selecting a subset of individuals from a population to represent the whole.
The process of summarizing data using descriptive statistics.
The process of analyzing data using inferential statistics.
#2
What is the population in statistical sampling?
A specific group of individuals from which data is collected.
The entire set of individuals or objects of interest.
The sample selected from a larger group.
The process of analyzing data using inferential statistics.
#3
What is the formula for calculating the sample mean?
Sum of sample values divided by sample size
Sum of population values divided by population size
Sum of sample values multiplied by sample size
Sum of population values multiplied by population size
#4
What is the formula for calculating the standard deviation of a sample?
Square root of the variance
Square root of the mean
Mean divided by the sample size
Variance multiplied by the sample size
#5
What is the formula for calculating the confidence interval of a sample mean?
Sample mean ± z-score * (standard error)
Sample mean * z-score / (standard error)
Sample mean * (standard deviation) / z-score
Sample mean / z-score * (standard deviation)
#6
What is sampling distribution?
A distribution of a sample statistic based on multiple random samples taken from a population.
A distribution of the entire population.
A distribution of a sample statistic based on one random sample taken from a population.
A distribution of the sample means.
#7
What is the key advantage of using random sampling techniques?
It guarantees a representative sample.
It is quick and easy to implement.
It eliminates bias and ensures fairness.
It allows for convenient selection of sample units.
#8
In statistical inference, what is a confidence interval?
The range of values within which a population parameter is estimated to lie.
A statistical measure used to summarize a set of data.
The process of collecting data from every individual in a population.
The process of selecting a subset of individuals from a population to represent the whole.
#9
What does the term 'margin of error' represent in statistical sampling?
The range of values within which a population parameter is estimated to lie.
The level of confidence associated with a statistical estimate.
The degree of variability in a sample statistic from sample to sample.
The amount by which a sample statistic may differ from the population parameter.
#10
Which of the following sampling methods is most likely to introduce selection bias?
Simple random sampling
Stratified sampling
Convenience sampling
Systematic sampling
#11
What is the purpose of stratified sampling?
To ensure that every individual in the population has an equal chance of being selected.
To divide the population into subgroups and then randomly select samples from each subgroup.
To select individuals who are most conveniently available.
To select every nth individual from a list of the population.
#12
Which of the following is an assumption of the central limit theorem?
The population must have a normal distribution.
The sample size must be small.
The population must have a finite standard deviation.
The sample must be non-random.
#13
What is the purpose of hypothesis testing in statistical inference?
To summarize data using descriptive statistics.
To estimate population parameters with a known degree of certainty.
To make inferences about population parameters based on sample data.
To determine if there is enough evidence to reject or fail to reject a null hypothesis.
#14
What is the null hypothesis typically denoted as in hypothesis testing?
#15
What does a Type II error represent in hypothesis testing?
Rejecting the null hypothesis when it is actually true.
Failing to reject the null hypothesis when it is actually false.
Failing to reject the alternative hypothesis when it is actually true.
Rejecting the alternative hypothesis when it is actually false.
#16
Which of the following statements about Type II error is true?
Type II error occurs when the null hypothesis is rejected when it is actually true.
Type II error occurs when the null hypothesis is not rejected when it is actually false.
Type II error is also known as a false positive.
Type II error is typically denoted by α (alpha).
#17
What is the primary difference between systematic sampling and stratified sampling?
Systematic sampling involves dividing the population into subgroups, while stratified sampling does not.
Stratified sampling selects every nth individual from a list of the population, while systematic sampling selects individuals from predetermined intervals.
Systematic sampling ensures representation from all population strata, while stratified sampling does not.
Stratified sampling involves selecting individuals based on convenience, while systematic sampling does not.