Type I: Rejecting a true null hypothesis (false positive). Probability is $\alpha$. | Type II: Failing to reject a false null hypothesis (false negative). Probability is $\beta$.

Random Sampling: Each member of the population has an equal chance of being selected. Reduces bias. | Convenience Sampling: Selecting participants based on ease of access. High risk of bias.

Leading questions: Prompt a specific response, introducing bias. | Neutral questions: Do not suggest a desired answer, minimizing bias.

The error caused by a sample not perfectly representing the population.

Inaccuracies in measuring variables, often due to confounding factors.

Systematic errors in sampling, measurement, or analysis, leading to skewed results.

Rejecting the null hypothesis when it is actually true (false positive).

Failing to reject the null hypothesis when it is actually false (false negative).

The probability of correctly rejecting a false null hypothesis.

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.

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.

Blocking groups subjects with similar characteristics together. This ensures that these variables don't skew your results.

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.

It can lead to missed opportunities, where a real treatment effect is not detected. This can prevent the adoption of beneficial practices or treatments.

Increasing sample size increases the power of a test, which decreases the probability of committing a Type II error.