A bell curve, or normal distribution, is a symmetrical curve with the mode (which also correlates to the mean and median) at the highest point of the curve. The mode is the value most frequently occuring in a set of variables. The mean is the average of a set of values. How spread out the curve is determined by the data set's standard deviation.

A learning curve plots one variable on the x-axis and another on the y-axis. An example of a learning curve could be experience plotted against learning. A person who learns quickly will have a learning curve convex to the vertical axis. A Venn diagram consists of two overlapping circles. In the overlap area, items that are similar to the data sets are included. Scatter plots show the observed relationships between different variables. A chi-squared test compares observed data with data the experimenter would expect to obtain. 

A type II error means that the experimenter has failed to recognize an effect that is present. On the other hand, a type I error indicates that the experimenter has mistakenly stated that the independent variable has an effect when it has no real effect.

Named after John Tukey, the Tukey test is based on a studentized range distribution included with many standard psychological and statistical textbooks today. It is flexible enough to be used with all types of pairwise comparisons, including ANOVA and multiple comparison tests; however, it suffers when sample sizes are unequal.

Nominal data is best represented by a bar chart. Nominal data can be variables such as religion. This is because there is no inherent rank. No one religion is statistically “better” than the other; therefore, it would be impossible to place one higher up than another in a rank order scheme. 

The correct choice is “independent variable.” The independent variable is changed in order to observe the changes, if any, that it has on the dependent variable or variables. The dependent variable is defined as the variable that changes in response to the independent variable.

The distance that any point is from the mean is known as the standard deviation. When looking at a normal distribution, the mean is the middle or arithmetic average of the set and the deviation measures the spread of the data points from this central tendency.

Ordinal data is used when variables that have an inherent natural order, such as the collegiate class structure: freshman, sophomore, junior, senior.  One must complete every level in order to transcend to the next. Nominal data deals with variables such as religion that do not have an inherent natural order. Interval data deals with things like temperature. Last, ratio data would deal with things like time. 

A normal distribution is asymptotic because it approaches a curve closely. It is unimodal because of its singular mode, and it is symmetric because both sides are mirror images of each other. There are other kinds of distributions like skewed distributions that are unimodal, asymptotic, and asymmetrical. These descriptions can vary depending on the shape of the distribution. 

A parameter measures a population—the whole group that is being considered. A statistic represents a measure of a sample—a subset of the group that is being considered. For example, if you were conducting a study on Spanish people in your university, then the population would be all of the Spanish people at your university. A sample could be all of the Spanish people in one of your classes.