AP Stats Unit 1 Practice Quiz

A sample is a subset of a population used to make inferences about the larger group. It is not the entire population and does not represent a complete census of all individuals.

The median is less affected by extreme values since it only depends on the middle value of a sorted list. Outliers can pull the mean significantly, making the median a more robust measure of center.

Histograms are ideal for displaying the distribution of continuous data because they show the frequency of data points within specified intervals (bins). This visual representation makes it easier to identify the shape and spread of the data.

Standard deviation quantifies the amount of variation or dispersion of data values around the mean. A larger standard deviation indicates that the data points are spread out over a wider range of values.

Quantitative discrete variables consist of countable numbers and typically represent counts. They have distinct, separate values unlike continuous variables, which can take on any value within a range.

The z-score is calculated by subtracting the mean from the data value and then dividing by the standard deviation. Here, (60 - 50) / 5 equals 2.

The box in a box plot shows the interquartile range (IQR), which is the difference between the 75th and 25th percentiles. This illustrates the spread of the middle 50% of the data.

Normal distributions are symmetric, bell-shaped, and have the mean, median, and mode all equal. High variability is not a defining feature of normality; rather, it pertains to the spread and scale of the data.

A correlation coefficient near 0 suggests that there is little to no linear relationship between the variables. It does not, however, rule out the possibility of a non-linear association.

Random assignment of participants to different groups is a hallmark of experimental studies. This design helps control for confounding variables and supports causal inference.

The range is calculated as the difference between the maximum and minimum values, making it very sensitive to extreme data points (outliers). Other measures like the IQR are more robust in the presence of outliers.

A parameter is a numerical summary that describes a characteristic of the entire population. Unlike a statistic, which describes a sample, a parameter pertains to the full population.

Scatterplots are ideal for displaying the relationship between two quantitative variables. They help reveal patterns, trends, and potential correlations in the data.

The p-value represents the probability of obtaining the observed data, or something more extreme, when the null hypothesis is true. It is a measure used to determine the strength of evidence against the null hypothesis.

Increasing the sample size reduces the standard error, which in turn narrows the confidence interval. A narrower interval indicates a more precise estimate of the population mean.

In a right-skewed distribution, extreme high values pull the mean upward, making it less representative of the data's center. The median, being less influenced by outliers, is a more appropriate measure.

A high correlation coefficient indicates a strong linear association but does not imply causation. Concluding that one variable causes changes in the other is a common misinterpretation of correlation.

When comparing the means of two independent groups, the two-sample t-test is the appropriate statistical method. It assesses whether the difference in sample means is statistically significant.

When sampling from a normally distributed population, the sampling distribution of the mean is also normally distributed regardless of the sample size. This is a key property used in inferential statistics.

Simple random sampling gives every individual in the population an equal chance of being selected, which minimizes selection bias. This method is most effective in producing a representative sample for estimating proportions.