Identifying Patterns MCQs – Class 10 Data Science (419)
Every MCQ of Identifying Patterns Class 10 DS (419) is carefully selected based on the latest CBSE syllabus, board paper pattern, and important concepts from the chapter. This collection Identifying Patterns MCQs is designed to give you complete practice and boost your confidence before the CBSE exam.
Q1. What is the deviation from the expected outcome in data called, in the context of Data Science?
a) Variance
b) Bias
c) Noise
d) Error rate
Q2. Bias in data basically occurs because of:
a) Poor programming logic
b) Sampling and estimation
c) Hardware malfunction
d) Network latency
Q3. Statement I: Bias can be considered a deviation from the expected outcome in data.
Statement II: Bias is always obvious and easily detected.
a) Both Statement I and Statement II are true
b) Both Statement I and Statement II are false
c) Statement I is true, but Statement II is false
d) Statement I is false, but Statement II is true
Q4. Data that is collected for a purpose other than modelling is referred to as:
a) Structured data
b) Found data
c) Sample data
d) Clean data
Q5. Which type of bias occurs when a model itself influences the creation of the data used to train it?
a) Recall Bias
b) Selection Bias
c) Linearity Bias
d) Survivor Bias
Q6. Which of the following biases is a cognitive bias rather than a statistical one?
a) Selection Bias
b) Linearity Bias
c) Recall Bias
d) Survivor Bias
Q7. Linearity bias is based on the wrong assumption that:
a) Data is always normally distributed
b) A change in one quantity produces an equal and proportional change in another
c) Sample size must always be 30
d) Bias never affects cognitive judgement
Q8. Confirmation Bias is also known as:
a) Recall Bias
b) Observer Bias
c) Survivor Bias
d) Selection Bias
Q9. A researcher begins a project already convinced of a certain outcome and unconsciously interprets the data to match this belief. This is an example of:
a) Recall Bias
b) Confirmation Bias
c) Linearity Bias
d) Survivor Bias
Q10. Statement I: Survivor bias can be reduced by studying failures as well as successful examples.
Statement II: Survivor bias occurs because researchers focus only on unsuccessful examples.
a) Both Statement I and Statement II are true
b) Both Statement I and Statement II are false
c) Statement I is true, but Statement II is false
d) Statement I is false, but Statement II is true
Q11. Recall Bias is commonly encountered at which stage of a data science project?
a) Data collection stage
b) Data labeling stage
c) Model deployment stage
d) Data visualisation stage
Q12. Survivorship bias occurs when:
a) We focus only on successful examples and ignore the failures
b) We take multiple random samples
c) The sample size is very large
d) Data is completely free of error
Q13. The only way to avoid survivor bias in a system is to:
a) Study only competitors who succeeded
b) Ignore businesses that failed
c) Find as many inputs as possible and study failures as well as average performers
d) Increase the sample size of successful cases only
Q14. According to the Central Limit Theorem, as the sample size increases, the distribution of sample means approaches a:
a) Uniform distribution
b) Normal distribution
c) Binomial distribution
d) Skewed distribution
Q15. As per the Central Limit Theorem, the minimum sample size generally considered sufficient for the theorem to hold is:
a) 10
b) 20
c) 30
d) 50
Q16. The formula used to calculate the sample standard deviation (σx̄) is:
a) σ × √n
b) σ / √n
c) σ × n
d) σ / n
Q17. As the sample size increases, the error in estimating the population mean:
a) Increases
b) Remains constant
c) Decreases
d) Becomes negative
Q18. Which of the following is a practical, real-life application of the Central Limit Theorem?
a) Calculating the mean family income of a region
b) Sorting an array of numbers
c) Compiling a computer program
d) Designing a database schema
Q19. A population has a standard deviation of 10 kg and a sample size of 50. Which expression should be used to calculate the sample standard deviation according to the chapter?
a) 10 × √50
b) 10 ÷ √50
c) 50 ÷ 10
d) 10 + √50
Q20. Why is taking random samples useful when the population is extremely large?
a) It eliminates the need for any data
b) It provides a practical way to estimate population characteristics without measuring everyone
c) It guarantees that every individual will be selected
d) It makes the population smaller
Q21. Assertion (A): The Central Limit Theorem becomes useful when sample size is sufficiently large.
Reason (R): The distribution of sample means approaches a normal distribution as sample size increases.
a) Both A and R are true, and R is the correct explanation of A
b) Both A and R are true, but R is not the correct explanation of A
c) A is true, but R is false
d) A is false, but R is true
