7 Basic Statistical Applications Free Online FlashCards

What distinguishes a population from a sample?

A population is the complete group of interest; a sample is a subset selected from that population.

Which graphs suit categorical and grouped quantitative data?

Bar charts display categorical counts or percentages; histograms display quantitative data grouped into intervals.

How do the mean and median differ in resistance to outliers?

The mean uses every observation and is sensitive to extreme values; the median is more resistant to outliers.

How do discrete and continuous random variables differ?

A discrete random variable has countable possible values, while a continuous random variable can take values throughout an interval.

What are random sampling and random assignment used for?

Random sampling selects units from a population and supports generalization; random assignment places units into treatments and supports causal comparisons.

What does the Central Limit Theorem state?

The Central Limit Theorem says that, under suitable conditions, the sampling distribution of a sample mean becomes approximately normal as sample size becomes sufficiently large.

What is the general purpose of statistical simulation?

A simulation imitates a random process by generating outcomes, calculating a result, repeating many times, and summarizing the resulting distribution.

Why does association not by itself prove causation?

Association alone does not establish causation. A well-designed randomized experiment can support causal conclusions because random assignment tends to balance other factors.

What two basic rules must every probability satisfy?

A probability must satisfy $0\le P(A)\le 1$, and the probability of the entire sample space is $P(S)=1$.

What does the multiplication principle state?

If one task has $m$ possible outcomes and a second has $n$, the combined task has $mn$ possible outcomes.

How is conditional probability $P(A\mid B)$ calculated?

Conditional probability is $P(A\mid B)=\frac{P(A\cap B)}{P(B)}$, provided $P(B)>0$.

When are two events independent?

Events $A$ and $B$ are independent when learning that one occurred does not change the probability of the other: $P(A\cap B)=P(A)P(B)$.