MCQs

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Overall MCQs for Reference

What is an outlier in a dataset?
a. A data point significantly different from others
b. The average value
c. The most common value
d. A data point within the normal spread
Answer: acliffsnotes+1
Which statistical measure is least affected by the presence of outliers?
a. Mean
b. Median
c. Range
d. Standard deviation
Answer: btestbook
Outlier detection is important in data science because:
a. Outliers never affect models
b. Outliers can distort statistical results
c. Outliers always improve model accuracy
d. Outliers only impact visualization
Answer: bstatisticsbyjim
Which of the following is NOT a common method for detecting outliers?
a. Z-score
b. Box plot
c. Interquartile range (IQR)
d. Central Limit Theorem
Answer: dgeeksforgeeks+2
The Z-score method typically classifies a data point as an outlier if its Z-score is:
a. Greater than 2 or less than -2
b. Greater than 3 or less than -3
c. Greater than 1 or less than -1
d. Equal to 0
Answer: bgeeksforgeeks
In a box plot, outliers are often represented as:
a. The box edges
b. The median line
c. Dots outside whiskers
d. The area within the box
Answer: cexpressanalytics+1
Which of the following causes outliers in data?
a. Human error
b. Instrument error
c. Data processing error
d. All of the above
Answer: dthedocs.worldbank
What impact do outliers have on the mean of a dataset?
a. None
b. They pull the mean towards themselves
c. They have less effect than on the median
d. They reduce the mean
Answer: bedge.sagepub+1
Which method for measuring central tendency is best used when outliers are present?
a. Mean
b. Median
c. Mode
d. Range
Answer: btestbook
The interquartile range (IQR) is used to:
a. Find the average
b. Identify the spread affected by outliers
c. Identify the central tendency
d. Measure data spread while ignoring outliers
Answer: donline.stat.psu+1
When applying the standard deviation method, how are outliers defined?
a. Data points outside three standard deviations from the mean
b. Data points within one standard deviation
c. Data points at the mean
d. Data points equal to the median
Answer: ageeksforgeeks
Which visualization is commonly used to detect outliers in two-dimensional data?
a. Histogram
b. Scatter plot
c. Pie chart
d. Bar chart
Answer: bexpressanalytics
What is a robust method for scaling data in the presence of outliers?
a. Min-max scaling
b. Robust scaling
c. Normal scaling
d. Log scaling
Answer: binterviewkickstart
A data point has a Z-score of 4.0. What does this usually indicate?
a. It is very close to the mean
b. It is likely an outlier
c. It is a common value
d. It is at the median
Answer: bgeeksforgeeks
Why should outliers often be removed from datasets used for machine learning?
a. They add noise, skewing results
b. They always improve accuracy
c. They do not affect the model
d. Models require outliers
Answer: acoursehero
The presence of an outlier in a data set will have the least effect on:
a. Standard deviation
b. Mean
c. Mode
d. Range
Answer: ctestbook
Which method can visually show outliers in univariate data?
a. Scatter plot
b. Box plot
c. Bar chart
d. Line plot
Answer: bexpressanalytics
The IQR method defines outliers as data:
a. Inside 1.5 IQR of Q1 and Q3
b. Outside 1.5 IQR of Q1 and Q3
c. Only above Q3
d. Only below Q1
Answer: bonline.stat.psu
Which of the following best describes an outlier?
a. Data point completely separate from the bulk of data
b. Central value
c. Data point close to median
d. Most frequently occurring value
Answer: aexamveda+1
What effect do outliers have on regression analysis?
a. They can distort regression parameters
b. No effect
c. Only affect dependent variable
d. Only affect independent variable
Answer: aedge.sagepub
Which technique should NOT be used if the data contains outliers?
a. Mean imputation
b. Median imputation
c. Robust regression
d. Box plot
Answer: atestbook+1
The first step in identifying outliers is to:
a. Visualize the data
b. Delete the data
c. Normalize the data
d. Encode categorical features
Answer: astatisticsbyjim
When should outliers be retained in analysis?
a. When they represent valid variation
b. When they are due to error
c. When they are unexplainable
d. Always
Answer: athedocs.worldbank
Outliers in a training dataset can:
a. Cause underfitting
b. Cause overfitting
c. Have no effect
d. Always improve data quality
Answer: bcoursehero
A dataset has 99 values close to 10 and one value of 1000. What is 1000 likely considered?
a. Median
b. Outlier
c. Mode
d. Standard value
Answer: bcliffsnotes+1
Which method is less effective if data is heavily skewed?
a. Z-score method
b. IQR method
c. Box plot method
d. Mode method
Answer: ascribbr
If an outlier results from a data entry mistake, what should be done?
a. Correct or remove it
b. Always keep it
c. Ignore it
d. Guess its value
Answer: athedocs.worldbank
Outlier detection in multivariate data often uses:
a. Mahalanobis distance
b. Mean
c. Median
d. Mode
Answer: aexpressanalytics
Which of the following is not a primary cause of outliers?
a. Instrument error
b. Random variation
c. Consistent data trend
d. Data entry error
Answer: cthedocs.worldbank
Which is most robust to the influence of outliers?
a. Mean
b. Standard deviation
c. Median
d. Variance
Answer: ctestbook+1
The k-nearest neighbor (k-NN) algorithm detects outliers by:
a. Analyzing distance to neighbors
b. Using mean
c. Using mode
d. Calculating range
Answer: ageeksforgeeks
Which summary statistic is most influenced by a single, extreme outlier?
a. Mode
b. Median
c. Mean
d. IQR
Answer: ctestbook
Outlier detection can help in:
a. Improving data quality
b. Worsening data quality
c. Making data incomprehensible
d. Hiding model performance
Answer: acoursehero
In the presence of outliers, which accuracy metric is usually preferred for regression?
a. Mean squared error (MSE)
b. Median absolute error (MedAE)
c. R-squared
d. Mean
Answer: bgeeksforgeeks
Which is a valid reason NOT to remove an outlier?
a. It is a correct and important observation
b. It results from measurement error
c. It is caused by data entry mistake
d. None of the above
Answer: athedocs.worldbank
What is one danger of ignoring outliers?
a. Skewed interpretation of results
b. Faster computation
c. Reduced data spread
d. Improved model accuracy
Answer: acoursehero
Which is NOT a visual technique for outlier detection?
a. Box plot
b. Scatter plot
c. Histogram
d. Fourier transform
Answer: dstatisticsbyjim+1
Which method resists the effect of outliers during data normalization?
a. Min-max scaling
b. Robust scaling
c. Z-score scaling
d. Standard deviation scaling
Answer: binterviewkickstart
Which of the following statements about outliers is correct?
a. Outliers may be valid data
b. Outliers should always be removed
c. Outliers never impact analysis
d. Outliers are always errors
Answer: athedocs.worldbank
Outliers are less likely to occur in data that is:
a. Highly controlled
b. Collected in real time
c. From manual entry
d. Taken from sensors
Answer: athedocs.worldbank
Which algorithm is sensitive to outliers in clustering?
a. K-means
b. DBSCAN
c. K-medoids
d. Hierarchical clustering
Answer: aexpressanalytics
What is the primary statistical reason for identifying outliers?
a. Ensuring statistical assumptions are not violated
b. To increase variance
c. To change sample size
d. For feature encoding
Answer: ageeksforgeeks
An outlier with a legitimate cause should be:
a. Retained
b. Deleted
c. Ignored
d. Interpolated
Answer: athedocs.worldbank
Which is a potential result of removing too many outliers?
a. Loss of important information
b. Improved accuracy
c. Always better model performance
d. No effect
Answer: acoursehero
The 1.5*IQR rule is often used to:
a. Identify outliers
b. Calculate means
c. Normalize data
d. Encode categorical data
Answer: aonline.stat.psu
For a dataset with many outliers, which is least appropriate?
a. Median
b. Mean
c. Mode
d. IQR
Answer: btestbook+1
Outliers impact which of the following the most?
a. Median
b. Mode
c. Mean
d. Interquartile range
Answer: ctestbook
What is one risk of removing all outliers automatically?
a. Losing valid rare events
b. Improving detection
c. Increasing variance
d. Lowering median
Answer: acoursehero
The Tukey’s method for detecting outliers uses:
a. Z-score
b. IQR fences
c. Standard deviation rule
d. Mean calculation
Answer: bonline.stat.psu
In highly skewed data, which transformation is most appropriate for reducing the effect of outliers?
a. Log transformation
b. Min-max scaling
c. One-hot encoding
d. Simple mean
Answer: aplacementpreparation

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