Quantitative Data:  Numeric data values.  i.e. heights of female volleyball players.  

Qualitative Data:  Data values that may be categorized according to a characteristic.  i.e. hair color of  secretaries. Sometimes called categorical data or categorical values.   

Independent Variable:  A variable that is manipulated by an experimenter.  

Dependent Variable:  The variable on which we examine the impact of the manipulation of the independent variable.  I.e.  If we change the Independent variable, do we see a change in the dependent variable?  

Discrete Variables:  Variables that assume values that can be counted and will assume a succinct value.  i.e.  number of days it rained in Hawaii in 2005.  

Continuous Variables:  Variables that can assume all values between any two given values - i.e. the time it takes for you to do your summer reading.  

Population:  All elements that are being studied comprise a population.  I.e. If we want to study the distribution of IQ scores among politicians in Florida, the population will be the IQ scores.  In statistics, the way we approach a problem and the formulas we use are completely dependent on whether we are examining a population or a sample.  

Sample:  A subset of the population.  I.e., Listing the IQ scores of every tenth politician in Florida from an alphabetical listing of politicians.  In statistics, the way we approach a problem and the formulas we use are completely dependent on whether we are examining a population or a sample.  

Census:  A sample of the entire population.  I.e. We will list the IQ scores for all politicians in Florida.

Parameter:  A characteristic of a population.  I.E.  the average IQ score of politicians in Florida.

Statistic:  A characteristic of a sample.  I.E.  The average IQ score for every tenth politician in Florida. 

Random Sample:  A sample selected so that each group (of equal size) has the same chance of being selected .