6.7 Potential Errors When Performing Tests

• A Type I Error is also known as a false positive
• The independence condition in statistical inference requires observations to be correlated.

  False
• Ordering the steps to address bias in research:

  1️⃣ Identify the source of bias

  2️⃣ Redesign the study to eliminate bias

  3️⃣ Collect unbiased data

  4️⃣ Analyze the unbiased data

  5️⃣ Draw valid conclusions
• Selection bias occurs when data is collected inaccurately.

  False
• What are the key assumptions and conditions in statistical inference?

  Randomness, independence, large sample size
• What happens if the p-value is less than alpha in hypothesis testing?

  Reject the null hypothesis
• What is one strategy to reduce errors in statistical inference?

  Increase sample size
• A Type II Error occurs when the null hypothesis is false, but we fail to reject it.


  True
• What does the condition of randomness ensure in statistical inference?

  Reduces selection bias
• What is another term for a Type I Error?

  False positive
• What type of bias occurs when an extraneous variable affects both the independent and dependent variables?

  Confounding bias
• The significance level, denoted by alpha ($\alpha$), is the probability of rejecting the null hypothesis when it is actually true
• The relationship between Type II error and power is complementary
• Increasing the power of a test reduces the chance of a Type II error
• What is a Type I Error in hypothesis testing?

  Rejecting a true null hypothesis
• Match the type of bias with its definition and example:

  Selection Bias ↔️ Non-representative sample ||| Surveying health food store visitors for health consciousness

  Measurement Bias ↔️ Inaccurate data collection ||| Using a faulty scale to measure obesity rates

  Confounding Bias ↔️ Extraneous variable affects both independent and dependent variables ||| Age influencing physical activity and heart disease
• For proportions, a large sample size is required, specifically $np \geq 10$ and $n(1 - p) \geq 10$
• Confounding bias occurs when an extraneous variable affects both the independent and dependent variables
• Measurement bias results from inaccuracies in how data is collected
• For proportions, a large sample size requires np ≥ 10 and n(1 - p) ≥ 10.


  True
• Power is the probability of correctly rejecting a false null hypothesis.


  True
• Lowering alpha increases the chance of a Type II error.


  True