4. Probability

Consider the following probability distribution for a discrete random variable $X$: $X$: $1$ $2$ $3$ $4$, $P\left(X\right)$: $0.1$ $0.3$ $0.4$ $0.2$. Which of the following statements is true about this distribution?

In the context of probability and statistics, what does it mean for a sociologist to control for a variable when analyzing data?

In the context of probability and statistics, what does it mean if a statistic is resistant?

Suppose you are counting the number of trials needed to get the first success in a sequence of independent $Bernoulli$ trials, where each trial has the same probability of success. Is it appropriate to use the $geometric$ distribution to calculate probabilities in this situation?

Which term best describes the process of finding patterns and anomalies (irregularities) in data to predict outcomes?

Which of the following is true of statistical forecasting methods that capture historic trends?

Which of the following is not a characteristic of the $t$ test?

In the context of continuous probability distributions, the area under the entire probability density curve is equal to which of the following values?

In statistics, why is a $\mathrm{sample}$ used more often than a $\mathrm{population}$ when conducting probability studies?

Which of the following is a possible probability distribution for a discrete random variable $X$ with possible values $1$, $2$, and $3$?

In a study, the mean height of all students in a university is calculated using the heights of every student. Is this mean value a $\mathrm{parameter}$ or a $\mathrm{statistic}$?

Which of the following is a qualitative measurement in the context of probability?

The Monte Carlo fallacy would most likely lead you to:

Which of the following situations cannot be described by a $discrete$ probability distribution?

Which of the following correctly represents the sample space $S$ for a single toss of a fair coin?