Final answer:
To ensure an unbiased estimate, use a random sample and ensure a large enough sample size.
Step-by-step explanation:
To ensure an unbiased estimate of a population parameter, we must use a random sample, where every member of the population has an equal chance of being selected. This helps to avoid any biases or preferences in the sample that could skew the results. To ensure a reliable estimate, we must also have a large enough sample size to minimize sampling error. A larger sample size reduces the variability and increases the accuracy of the estimate.
To provide an unbiased and precise estimate, use a random and sufficiently large sample, calculate a point estimate, and then determine a confidence interval and error bound.
To ensure an unbiased estimate of a parameter, we must use a random sample. To ensure a precise estimate, we must use a larger sample size. When estimating, it is critical to have clear ideas about how variables may be related and to follow sound statistical practices.
Estimation Strategies
Find the point estimate for the population mean.
Using 95 percent confidence, calculate the error bound.
Create a 95 percent confidence interval for the mean.
Interpret the confidence interval in the context of the problem.
For example, the mean rent for a two-bedroom apartment can be estimated using classified ads to obtain a point estimate. Similarly, the success rate of basketball shots can be estimated by calculating a proportion based on shots made versus attempted.
Consider whether a sample is unbiased and representative. For instance, while a larger sample increases the chance of representing the population accurately, a biased sampling technique could result in an unreliable estimate, even with a large sample size.