Data Visualization MCQs

Total probability across the complete discrete sample space must equal one.
Individual probabilities must also lie between zero and one.

A fixed seed lets collaborators recreate the same simulated sample.
This supports verification and diagnosis of computational workflows.

Chart junk consumes attention without adding information.
Examples include unnecessary gradients, textures, and three-dimensional effects.

is.na() tests each element for the NA missing-value marker.
The result can be used for filtering or counting missing observations.

sample() draws elements with or without replacement.
It is commonly used for resampling, permutation, and simulation.

EDA helps analysts understand what the data contains before choosing a model.
It emphasizes discovery, diagnosis, and informed question refinement.

Decision-oriented visualization should foreground the quantities and trade-offs that matter.
Irrelevant detail can obscure actionable evidence.

Setting na.rm to TRUE removes NA values for the calculation.
Without it, a missing value usually causes mean() to return NA.

summary() reports values such as the minimum, quartiles, median, mean, and maximum.
It offers a quick numerical overview of a variable.

Initial inspection reveals the structure and limitations of the dataset.
This prevents inappropriate summaries, charts, and transformations later.

Exploratory graphs support rapid investigation rather than polished publication.
They are often revised as the analyst learns more about the data.

Factors store categorical values together with their possible levels.
They are useful in summaries, models, and grouped visualizations.