Week 4 Error and Bias

What is the primary difference between random error and systematic error in research studies?

• Random error affects the accuracy of measurements, while systematic error affects the precision.

• Random error is caused by chance fluctuations, while systematic error is consistent and predictable.

• Random error can be controlled by increasing sample size, while systematic error requires adjusting study design

• Random error leads to biased results, while systematic error does not influence study outcomes.

Which of the following is an example of selection bias in a research study?

• Participants in the control group are older than those in the intervention group

• Only participants who complete the full study protocol are included in the analysis

• Participants with a particular disease are more likely to be included in the study sample

• Researchers use a reliable measuring instrument to collect data from all participants

How can confounding variables be controlled in a research study?

• By randomizing participants into treatment groups.

• By using blinding techniques during data collection

• By excluding participants with potential confounders.

• By adjusting for confounders in statistical analysis.

Which of the following statements best describes how blinding helps reduce bias in a research study?

• Blinding ensures that participants are randomly assigned to treatment groups

• Blinding prevents researchers from knowing which participants are in each group.

• Blinding eliminates confounding variables from affecting study outcomes.

• Blinding increases the precision of measurements in data collection.

What is an example of information bias in a survey study?

• Participants provide inaccurate information about their age.

• Researchers use a validated questionnaire to collect data

• Data collectors are unaware of participants' group assignments.

• Study participants have different levels of exposure to the intervention

How does randomization help control for potential biases in experimental studies?

• By ensuring that participants are representative of the population.

• By minimizing the influence of confounding variables on treatment assignment.

• By standardizing data collection procedures across study groups.

• By blinding participants and researchers to group assignments.

What is a Type I error in hypothesis testing?

• Rejecting a true null hypothesis

• Failing to reject a false null hypothesis

• Accepting an alternative hypothesis

• Obtaining inconclusive results

Which of the following statements is true regarding Type II errors?

• Type II errors occur when the null hypothesis is true

• Type II errors are more serious than Type I errors.

• Type II errors involve rejecting a false null hypothesis.

• Type II errors are denoted by alpha (α) in statistical notation

In a medical diagnosis scenario, what does a Type I error represent?

• Incorrectly diagnosing a patient as healthy when they have the disease

• Incorrectly diagnosing a patient as having the disease when they are healthy

• Correctly diagnosing a patient as having the disease

• Correctly diagnosing a patient as healthy

What is the relationship between Type I and Type II errors?

• They are independent of each other.

• Decreasing one type of error increases the other type.

• They both have the same probability of occurrence.

• Controlling Type I errors automatically controls Type II errors.