stats exam one

stats

set of mathematical procedures for organizing, summarizing, and interpreting information

population

entire set of individuals of interest

sample

set of individuals of interest selected from the population

variable

characteristics that can vary in values for different individuals. no manipulation or calculation

data

measurements and observations of variables

dataset

collection of data or list of numbers

score/raw score

single measurement/observation

statistic

value that describes a sample

parameter

value that describes a population

descriptive stats

used to summarize, organize, simplify data

inferential stats

allow us to use samples to make generalizations about the population

sampling error

error that exists between sample stats and population parameters

independent variable

manipulated by experimenter before observation of DV

dependent variable

observed variable. not manipulated

discrete variable

no values exists between different levels of variable. only whole numbers

continuous variable

infinite number of values exist between different levels of variables. can be decimals

nominal

categories are mutually exclusive and not ordered. species of animal

ordinal

order matters but the difference is between values is not informative. place in a race

interval

order matters but difference between values is informative. temperature- there no such thing as no temperature

ratio

interval variable and true definition of zero. zero pieces of candy eaten

frequency table

organizes a dataset by illistrating the frequency of each value for a variable

bar graph

used to graph nominal and ordinal data. x-axis (categories) y-axis (numbers)

histogram

used to graph interval and ratio data x-axis (frequency) y-axis (values)

normal curve

one peak, symmetrical, most values fall under

skewed curve

scores tend to pile up on one end of scale

positive (right) skew

tail is on right end of scale. curve is on left

negative (left) skew

tail is on left end of scale. curve is on right

kurtosis

heaviness of the tails of a distribution

leptokurtic

higher peak, heavier tails

playkurtic

flatter peak, lighter tails

central tendency

stats measure that attempts to determine the single value that is most represented of a set of scores (can be 2 scores)

measures of central tendency

mean, median, mode

mean

average of all scores. sum of all scores divided by number of scores

population mean formula

sample mean formula

M= sum of all (X)/ n

median

midpoint of all scores. used when extreme scores/skewness of distribution

mode

score that has the greatest frequency/ use when nominal and discrete variables

measures of variability

measure of differences among scores in a dataset. degree to which scores are spread out or clustered together. range, SD, variance

range

distance covered by scores in a distribution. calculate smallest score from largest score

standard deviation

determines how far the average score in a dataset is from the mean

variance formula

SD squared

standard deviation formula

square root of variance

deviation

distance between a score and the mean

variance formula population

sum of all (x- mu) squared/ N

z scores

indicated the location of each raw score within a distribution. how many SD above or below the mean a score is (can be positive or negative)

magnitude

how near (smaller) or far (larger) the raw score is from the mean

z score formula

z= x- mu/ sigma

z score distribution

every raw score has been transformed into corresponding z score. shape always stays the same

mean of z distribution is

0

SD of z score distribution is

1