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independent variables researcher manipulates/researcher can assign you/ cause (not result)
dependent variable measuring behavior/ result (not cause)
correlation 2 variables that are related
population complete collection of anything regardless of size
sample subset of population/ measure so we can talk about population
parameter numerical summary characteristics of a population stuff you can measure about a population
statistics numerical summary of characteristics of a sample
nominal scale (scale of measurement) assigns names or label to objects or events not related numerically just assigned
ordinal scale (scale of measurement) puts objects/events in RANK order the difference btwn 1&2 doesn't mean anything just the order is meaningful
interval (scales of measurement) equal intervals btwn numbers rank & equal distance zero isn't true zero, its a point on a scale
ration (scales of measurement) zero means absence of what your measuring true zero absolute 0 degrees K no heat height 0.00 inches quantity/time
frequency distribution create a table of the data gives organization table shows # of times a given score/ group of score occurs
rules of graphing x= variable being studied y-axis=should be 3/4 the the length of the x-axis *always be divided using full range of frequency *always begin y-axis at zero *if your using top half, indicate a break
bargraph histogram but bars dont touch
histogram depicts grouped frequency distribution
grouped frequency distribution range grouped data
central tendency trying to come up with 1 number to describe variables mode mean median
mode most frequently occurring score in a group of scores x
median score that separates top half of group of scores from bottom half normal curve-median & mode in same place *data has to be in order middle score
percentile score at or below which a given percentage of the scores lie 50th percentile: 50% of score are below that score
mean arythmetic average/ sum of scores divided by number of scores (sum)(x-score)/N-number
mean properties if you take all score the sum of the difference between each score and mean will be 0 sum of squared differences btwn each score from mean will be smaller than sum of squared diff. of any score 1,2,3,4,5 5-3=2 4-3=1 3-3=0 3-4=-1 3-5=-2
z-score aka standard scores the deviation of the raw from the mean in standard deviation units z=raw score(x)-(mean)/SD
probability the probability of event occurring is proportion of times event would occur if chance of occurrence were infinite
statistical hypothesis hyp- guess about population based on sample results use probability theory to determine degree of certainty we have abt experimental results
2 rules of probability 1. each event independent from each other/ stand alone. necessary for simple probability 2. dice or coin dont have memory and cant remember or keep track
gamblers phallacy (wrong belief) mistaken belief that probability of particular event changes with long strain of some event i.e. slot machines
addition rule mutually exclusive random events compute the probability of one OR the other occurring P(A or B)= PA + PB 2 of spaids or 3 of clubs 1/52 + 1/52 = 2/52 = 1/56
multiplication rule probability of 2 or more independent events occurring is the product of individual probabilities P rolling 2 ones (snake eyes) (1/6) (1/6) = 1/36
non independent events *conditional probability drawing card and holding onto it draw ace and draw another one (4/52)(3/51)=1/221= .0045= .45% probability of second dependant on first
confidence interval specific kind of interval estimate interval estimate of population can define by how "confident" compute a way to estimate things about a population
interval range (set) of numbers
sampling distribution of mean distribution of means of many samples u= mean of population
sampling distribution of mean (rules) 1. if you have infinite #, mean of means would be u 2. the more samples you get, the more the frequency graphs will look like normal curve 3. SD of means= standard error of mean 4. the larger each sample size, the smaller the standard error of mean
central limit theorem the more samples you get, the more the frequency graphs will look like normal curve
t-score how far a group mean is from population mean in standard error units
variable anything that can take on different values or amounts
Created by: frankiegym