Data Visualization MCQs

Robust methods are less distorted by skewness and extremes than mean-based rules.
Visual inspection helps account for the distribution's actual shape.

Robust scale measures such as IQR or median absolute deviation limit outlier influence.
They are useful for skewed or contaminated data.

A finite sample may not contain an observation exactly at the desired position.
Different quantile algorithms interpolate between ordered values differently.

The empirical 68-95-99.7 rule summarizes normal coverage.
Roughly 68 percent lies between mean minus and plus one standard deviation.

Large high-end values pull the mean toward the right tail.
The median is less affected, so it often remains below the mean.

Datasets with the same center may have different variability, skewness, or modality.
A useful summary usually combines center with spread and a distributional display.

Stacked bars display both total magnitude and categorical composition.
However, only segments sharing a baseline can be compared precisely.

Context can make a value normal in one setting but abnormal in another.
For example, a temperature may be ordinary in summer but unusual in winter.

The MAD uses the median as both its center and aggregation mechanism.
This makes it much more resistant to extreme values than standard deviation.

The empirical CDF increases by the observed sample proportion at each value.
It estimates the population CDF without assuming a parametric model.

Standardization converts a normal variable to zero mean and unit standard deviation.
Its values are interpreted as z-scores.

Skewness is named for the direction of the longer tail.
A left-skewed distribution has its extended tail on the low-value side.