Sampling Error and confidence intervals

10 important questions on Sampling Error and confidence intervals

Why is precise information about the variation in values of M due to sampling error important?

Because of the set up of confidence intervals which tells us about the amount of sampling error we need to take into account when we use on sample value of M to try to estimate μ (mu) for a population. and for the set up of statistical significance test.

What is a prediction Error?

When you uese M forma on e sample to estimate μ, there is always an error. The difference from M and μ (m-μ) is the prediction Error.

When does the population standard error (Qm) increase?

If N is held constant, but the scores of the population increases.
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Why would we like to have a small value of the population standard error (Qm)?

Because a small distance between the population mean and the sample mean indicates less predation error.

What will happen to the standard deviation of the mean (Qm) when other factors being equal, as N increases

It wil decrease

What is the beste way to decrease the standard deviation of the population mean (Qm)?

To increase the N (sample size)

Difference between normal distribution and a t distribution

A normal distribution always have the same shapes and percentages. Whereas a t distribution a flatter curve and the curve is not always the same because the degrees of freedom can changes which have an impact on the shape of the distribution.

Which 3 steps do you need for reading  t distribution table?

1. What percentage of area will you use to define close, named confidence interval.
2 find degrees of freedom of sample (N-1)
3. Locate the cell in the row and collum

When other factors are equal other factors make confidence intervals wider, which factors are they?

A smaller N and a larger standard deviation

How to interpret a confidence interval

For 95% CI are predicted to include the population mean between the lower and upper limit. Around 5% of these CI's will not contain the population mean in 1000 cases.

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