Statistics and Parameters Quiz b1

What is the main difference between a statistic and a parameter?

• A statistic describes a sample, while a parameter describes a population.

• A statistic describes a population, while a parameter describes a sample.

• A statistic is always accurate, while a parameter is estimated.

• A parameter changes frequently, while a statistic remains constant.

Which of the following is an example of a population parameter?

• The average salary of 500 randomly selected employees in the UAE.

• The average height of all students in a class.

• The median income of all residents in Dubai.

• The percentage of students who pass an exam out of a selected group.

If a company surveys 200 customers about their satisfaction level and calculates the average rating, this is an example of a:

• Parameter

• Statistic

• Population Mean

• Confidence Interval

Which of the following distributions is often used to represent real-world continuous data like heights or IQ scores?

• Uniform Distribution

• Normal Distribution

• Poisson Distribution

• Binomial Distribution

What does it mean when a dataset follows a normal distribution?

• The data is skewed toward the right.

• Most data points cluster around the mean, forming a symmetric bell shape.

• The values are spread evenly across all possible outcomes.

• The data has equal probability of any value occurring.

In a normal distribution, what percentage of data falls within one standard deviation (±1σ) of the mean?

• 50%

• 68%

• 95%

• 99.7%

What happens to the confidence interval when we increase the sample size?

• It gets wider.

• It gets narrower.

• It remains the same.

• It depends on the data values.

If a 95% confidence interval for the average spending in a shopping mall is (AED 200, AED 300), what does this mean?

• 95% of customers spend between AED 200 and AED 300.

• There is a 95% chance that the population mean falls between AED 200 and AED 300.

• The sample mean is exactly AED 250.

• The confidence level will always be 100%.

Which of the following statements about sampling distributions is true?

• A sampling distribution is always normal.

• A larger sample size results in a more accurate sampling distribution.

• A sample mean is always equal to the population mean.

• The shape of the sampling distribution depends on the original population.

What is the key assumption of the Central Limit Theorem?

• The population must always be normally distributed.

• The sample size should be large (n ≥ 30) for the sampling distribution to be approximately normal.

• The sample must always be random.

• The mean and standard deviation must be equal.