Data Bias: Multiple Choice Questions for Computer Science Students

📚 Quick Study Guide

• 📊 Definition: Data bias refers to systematic errors in data that skew results and lead to inaccurate conclusions.
• 🔍 Sources of Bias:
  • Sampling bias: Data not representative of the population.
  • Measurement bias: Errors in how data is collected.
  • Algorithmic bias: Bias introduced by the algorithm itself.
• 💡 Impact of Bias:
  • Unfair or discriminatory outcomes.
  • Inaccurate predictions.
  • Poor decision-making.
• 🛠️ Mitigation Strategies:
  • Careful data collection and cleaning.
  • Bias detection algorithms.
  • Regular auditing and monitoring.

🧪 Practice Quiz

1. Which of the following best describes data bias?

   • A. Random errors in data entry.
   • B. Systematic errors that skew results.
   • C. The use of outdated data.
   • D. The absence of data.

2. Sampling bias occurs when:

   • A. Data is collected using precise instruments.
   • B. The data is not representative of the population.
   • C. The data is analyzed using complex algorithms.
   • D. The data is stored in a secure database.

3. Which of the following is an example of measurement bias?

   • A. Using a biased algorithm.
   • B. Collecting data from a representative sample.
   • C. Using a faulty measuring device.
   • D. Excluding outliers from the dataset.

4. Algorithmic bias refers to:

   • A. Bias introduced by the algorithm itself.
   • B. Bias in the data collection process.
   • C. Bias in the interpretation of results.
   • D. Bias in the selection of the sample population.

5. What is a potential impact of data bias?

   • A. Fair and equitable outcomes.
   • B. More accurate predictions.
   • C. Unfair or discriminatory outcomes.
   • D. Improved data quality.

6. Which of the following is a strategy to mitigate data bias?

   • A. Ignoring outliers in the data.
   • B. Using biased algorithms.
   • C. Careful data collection and cleaning.
   • D. Relying solely on automated data analysis.

7. Why is it important to address data bias in machine learning models?

   • A. To increase the complexity of the models.
   • B. To ensure the models produce fair and accurate results.
   • C. To reduce the amount of data required for training.
   • D. To speed up the training process.