Explain the concept of alpha level ($\alpha$).
The probability of making a Type I error. A common value is 0.05, meaning there is a 5% chance of rejecting a true null hypothesis.
Explain how random sampling minimizes bias.
Random sampling gives each member of the population an equal chance of being selected, reducing the likelihood of systematic differences between the sample and the population.
Explain how blocking minimizes confounding variables.
Blocking groups subjects with similar characteristics together. This ensures that these variables don't skew your results.
Explain the impact of a Type I error.
It can lead to false conclusions, where a treatment or effect is believed to exist when it actually does not. This can result in wasted resources or incorrect decisions.
Explain the impact of a Type II error.
It can lead to missed opportunities, where a real treatment effect is not detected. This can prevent the adoption of beneficial practices or treatments.
Explain how increasing sample size affects Type II error.
Increasing sample size increases the power of a test, which decreases the probability of committing a Type II error.
What is the definition of Sampling Error?
The error caused by a sample not perfectly representing the population.
What is the definition of Measurement Error?
Inaccuracies in measuring variables, often due to confounding factors.
What is the definition of Bias?
Systematic errors in sampling, measurement, or analysis, leading to skewed results.
What is a Type I error?
Rejecting the null hypothesis when it is actually true (false positive).
What is a Type II error?
Failing to reject the null hypothesis when it is actually false (false negative).
Define Power (statistical)
The probability of correctly rejecting a false null hypothesis.
What is the probability of making a Type I error?
Equal to the alpha level ($\alpha$).
How is Power related to Type II error probability ($\beta$)?
Power = 1 - $\beta$
