What is the difference between descriptive and inferential statistics?

What are operational definitions and why are they used?

Why is it important to identify the scale of measurement for a variable?

Distinguish between percentiles and percentile ranks.

What are the three primary characteristics of a frequency distribution graph?

Why do we measure central tendency?

In what ways can central tendency be measured? When is each measure of central tendency most appropriate?

How are measures of central tendency affected by the shape of a distribution and changes in the distribution (e.g., the presence of extreme scores, addition of scores, etc.)

What is meant by variability?

Define standard deviation and distinguish between standard deviation and variance.

Why do we divide SS by N when computing population variance, but divide SS by n – 1 when computing variance for a sample? Explain what it means for sample variance to provide an unbiased estimate of the population variance.

What are degrees of freedom?

How do changes in the data set (addition of scores, adding a constant to each score, etc.) impact the standard deviation?

How is variability influenced by extreme scores, sample size, sampling stability, and open-ended distributions?

What role does variability play in descriptive and inferential statistics?

Why do we use z-scores?

What are the properties of any distribution of z-scores? 

What are the properties of a normal distribution?

What is the difference between the normal distribution and the standard normal distribution?

What are the dangers of choosing a sample that is not random?

Distinguish between independent, dependent, and mutually exclusive outcomes.

Determine whether a variable is discrete or continuous.

Determine whether a variable is measured on a nominal, ordinal, interval, or ratio scale.

Determine whether something is an example of a descriptive or inferential statistic.

Determine whether research is correlational, experimental, or quasi-experimental.

Identify independent, dependent, and confounding variables.

Make all the various types of frequency distribution tables and be able to interpret information from the tables.

Make all various types of frequency distribution graphs (and know when to use each type).

Describe a frequency distribution graph with respect to its three defining characteristics.

Create a stem and leaf display.

Find a percentile rank or percentile (with and without interpolation).

Compute the mean, median, and mode for a given data set.

Compute the 5-number summary for a given data set.

Compute the range, interquartile range, semi-interquartile range, variance, and standard deviation for a data set. 

Understand how measures of central tendency & variability are influenced by changes in the data and know when to use each type of measure.

Determine the shape of a distribution from central tendency & variance information.

Transform X values into z-scores and transform z-scores into X values.

Use z-scores to make comparisons and to create standardized distributions with a given mean and standard deviation. Determine the probability of an event (or combination of events).

Use the unit normal table to determine the probabilities for events that are normally distributed.

Use the unit normal table to find the specific score associated with given probabilities or proportions. 

Find percentiles and percentile ranks for scores in a normal distribution.

Interpret SPSS output for descriptive statistics and frequencies.

empirical

population and sample

parameter and statistic

descriptive & inferential statistics

sampling error

random selection (sampling)

variable

correlation study

experiment

independent & dependent variable

experimental & control groups

between groups v. within groups designs

confounding variable

random assignment

quasi-experiment

differential  & time-series research

hypothesis

construct

operational definition

scales of measurement

discrete vs. continuous variables

real limits

frequency distribution table

simple frequency distribution (grouped & ungrouped)

relative frequency distribution (proportions & percents)

cumulative frequency distribution (simple frequencies)

cumulative relative frequency distribution (cumulative %’s)

percentile ranks and percentiles

interpolation

apparent vs. real limits

frequency distribution graphs: histogram, bar graph, polygon

relative frequency curves

symmetrical vs. skewed distributions

kurtosis

stem and leaf display

central tendency

mean & weighted mean

median

mode (unimodal, bimodal)

variability

range

interquartile & semi-interquartile range

5-number summary

standard deviation

variance

deviation score

sum of squares (SS)

degrees of freedom

biased & unbiased statistics

standard score

standardized distribution

z-score and z-distribution

probability

mutually exclusive outcomes

independent and dependent outcomes

addition and multiplication rules

sampling with replacement

normal distribution

standard normal distribution

unit normal table