Glossary
 Page ID
 17449
Words (or words that have the same definition)  The definition is case sensitive  (Optional) Image to display with the definition [Not displayed in Glossary, only in popup on pages]  (Optional) Caption for Image  (Optional) External or Internal Link  (Optional) Source for Definition 

(Eg. "Genetic, Hereditary, DNA ...")  (Eg. "Relating to genes or heredity")  The infamous double helix  https://bio.libretexts.org/  CCBYSA; Delmar Larsen 
Word(s) 
Definition 
Image  Caption  Link  Source 

Statistical analysis, statistical analyses 
Procedures to organize and interpret numerical information 
CCBY Michelle Oja  
Statistic  The results of statistical analyses  CCBY Michelle Oja  
Independent Variable, IV  The variable that the researcher thinks is the cause of the effect (the DV). The IV is sometimes also called a "predictor" or "predicting variable".  CCBY Michelle Oja  
Dependent Variable, DV 
The variable that you think is the effect (the thing that the IV changes). The DV is the outcome variable, the thing that you want to improve. 
CCBY Michelle Oja  
Sample, samples  People who participate in a study; the smaller group that the data is gathered from.  CCBY Michelle Oja  
Population  The biggest group that your sample can represent.  CCBY Michelle Oja  
Ratio  True numerical scale of measurement; type of variable that is measured, and zero means that there is none of it.  CCBY Michelle Oja  
Interval  Created numerical scale of measurement; type of variable that is measured, and the intervals between each measurement are equal (but zero does not mean the absence of the measured item).  CCBY Michelle Oja  
Ordinal  Scale of measurement in which levels have an order; a type of variable that can be put in numerical order. The variables are in ranks (first, second, third, etc.).  CCBY Michelle Oja  
Nominal  Scale of measurement that names the variable's levels; type of variable that has a quality or name, but not a number that means something.  CCBY Michelle Oja  
Quantitative variable, quantitative variables  Type of variable that is measured with some sort of scale that uses numbers that measure something.  CCBY Michelle Oja  
Qualitative variable, qualitative variables  Type of variable that has different values to represent different categories or kinds This is the same as the nominal scale of measurement.  CCBY Michelle Oja  
Frequency Table  Table showing each score in the “x” column, and how many people earned that score in the “f” column. The “x” stands in for whatever the score is, and the “f” stands for frequency.  CCBY Michelle Oja  
Outlier  An extreme score, a score that seems much higher or much lower than most of the other scores (There is a technical way to calculate whether a score is an outlier or not, but you don't need to know it.)  CCBY Michelle Oja  
Skew, skewed distribution  A distribution in which many scores are bunched up to one side, and there are only a few scores on the other side.  CCBY Michelle Oja  
Positive skew  The scores are bunched to the left, and the thin tail is pointing to the right.  Positive skew is shown on the right panel.  Rodolfo Hermans (Godot), CC BYSA 3.0 via Wikimedia Commons  CCBY Michelle Oja  
Negative skew  The scores are bunched to the right, and the thin tail is pointing to the left.  Negative skew is shown on the left panel.  Rodolfo Hermans (Godot), CC BYSA 3.0 via Wikimedia Commons  CCBY Michelle Oja  
Kurtosis  A measure of the “tailedness” of the distribution of data (how wide or broad the distribution is)  Example of different types of kurtosis.  Larry Green, CCBY  CCBY Michelle Oja  
Leptokurtic  A tall and narrow distribution of data.  The tallest (blue) line is a leptokurtic shape.  Larry Green, CCBY  CCBY Michelle Oja  
Platykurtic  A wide and flat distribution of data.  The lowest (red) line is a platykurtic shape.  Larry Green, CCBY  CCBY Michelle Oja  
Mesokurtic  A medium, bellsharped distribution of data.  The middle (black) line is a mesokurtic shape.  Larry Green, CCBY  CCBY Michelle Oja  
Frequency Distribution  A distribution of data showing a count of frequency (how many) for each score or data point.  CCBY Michelle Oja  
Range  The difference between the highest score and the lowest score in a distribution of quantitative data.  CCBY Michelle Oja  
Robust  A term used by statisticians to mean resilient or resistant to  CCBYNCSA Foster et al.  
Descriptive Statistics  Used to describe or summarize the data from the sample.  CCBY Michelle Oja  
Inferential Statistics  Used to make generalizations from the sample data to the population of interest.  CCBY Michelle Oja  
Parameter  Statistic describing characteristics of the population (usually mean and standard deviation of the population)  CCBY Michelle Oja  
NonParametric Analysis, nonparametric analyses  Statistical analyses using ranked data; used when data sets are not normally distributed or with ranked data  CCBY Michelle Oja  
Research Hypothesis  A prediction of how groups are related. When comparing means, a complete research hypothesis includes:

CCBY Michelle Oja  
Null Hypothesis  A prediction that nothing is going on. The null hypothesis is always:
1. There is no difference between the groups’ means OR 2. There is no relationship between the variables. 
CCBY Michelle Oja  
Absolute value  Any number converted to a positive value  https://crumplab.github.io/statistic...ibingData.html  CCBYSA Mattew J. C. Crump  
Main Effect  Any statistically significant differences between the levels of one independent variable in a factorial design.  CCBYSA Mattew J. C. Crump  
Interaction, interaction effect  How the levels of two or more IVs jointly affect a DV; when one IV interacts with the other IV to affect the DV.  CCBY Michelle Oja  
Positive Correlation  When two quantitative variables vary together in the same direction; when one increases, the other one also increases (and when one decreases, the other also decreases)  CCBY Michelle Oja  
Negative Correlation  When two quantitative variables vary in opposite directions; when one variable increases, the other variable decreases.  CCBY Michelle Oja  
Binary variable, binary  A binary variable is a variable that only has two options (yes or no). Binary variables can be considered quantitative or qualitative.  CCBY Michelle Oja  
Dichotomous variable, dichotomous  A dichotomous variable is a variable that only has two options (yes or no). Binary variables can be considered quantitative or qualitative.  CCBY Michelle Oja  