Descriptive Statistics
Allow us to describe a set of data we have
Mean
-The “balancing point” for distribution
-Impacted by high or low values
-Add all values up and divide by how many #’s there are
Median
-Distance above/below doesn’t matter
-The middle value in a data set when put in inc. order
-With even numbers add the 2 and divide by 2
Mode
The most commonly occurring value in the data.
Inferential Statistics (2)
-Allow is to generalize beyond our sample to the population
-Allow us to infer characteristics of a population based on an observable sample
Population
Everything we care about and what we want to know
Parameters
The numbers that summarize a population
Ex:The average GPA of all students at OSU
Sample
-The subset of things from a population (draw inferences)
-Representative of the population
Random Sampling
Random selection process so everyone has an equal chance of being selected for the sample
Ex:picking a name from a hat
Discrete Variable
This is a finite number of values like whole numbers (not infinite)
Ex:number of days per week you go to class (can’t go an infinite # of times) number of suitcases you take on vacation
Continuous Variable
This is a full range of values and an infinite number (can be broken up into fractions)
Ex:heights, temperature, amount of time
weight of tomatoes
Nominal
Unordered categories
Ex: soup of the day, its either one or the other
Ordinal
Ordered or ranked values
Ex:bronze, silver, or gold, greater or less than
Interval
Equal spacing between whole units and ranked
Ex:temperature and calendar years (from 2010 to 2015 is the same distance as 2015 to 2020)
Ratio
Ordered and uniformly scaled with equal spacing between units and a true 0 (twice as tall/half the height)
Ex:you can’t be 0 inches tall
Independent Variable
This is manipulated
Dependent Variable
This is measured
Reliability
A measure that is consistent and a prerequisite for validity
Validity
A measure that accurately measures what it’s supposed to measure
Correlational Studies
If and how strongly are two variables related to each other without manipulating them
Nonequivalent groups
The researcher doesn’t control which participants go into which group
Pre-post study
Two groups of scores are obtained by measuring the same variable twice for each participant, once before treatment and once after
Random Assignment
Randomly assigning participants from the sample to different groups
Between-groups design
Each participant experiences only one IV
