Psych Stats Quiz #1 4/3/23 Flashcards
descriptive stats
used to summarize and describe a set of data in a clear and convenient fashion usually through numerical values
ex of descriptive stats
GPA - summarizes all grades your earned in college
inferential stats
because its not practical/realistic/ethical to measure everyone in a population inferential stats draws inferences about a population based upon an observed sample
ex of inferential stats
smoker study - studying a subset of smokers to apply observed data to the greater smoking population
sample
subset of scores from a population - measure relatively small group of people
population
collection of measurements that share a common characteristic, not necessarily people
random sample
selected so that every score in the population has an equal chance of being included
ex. sample of smokers for the smoker study
variability
people (observations) differ from one another
describes the extent to which scores in a group differ from one another or how spread out or scattered they are
reasons for variability
individual differences
unsystematic differences in procedure
research manipulation
measurement
the use of a rule to assign a number to a specific observation of a variable
ex. length or weight
measurement scale property: category
observations assigned a different number representing the category
measurement scale property: ordinal
can be used to measure those with the least amount to the most amount
measurement scale property: equal intervals
1 unit difference remains constant
measurement scale property: absolute zero
0 represents absence of something
type of scale: nominal
categories - qualitative but not quantitative
ex. breed of dog
type of scale: ordinal
ranks
ex. class rank = 1 vs 2 vs 3 but doesn’t describe the interval between the ranks
type of scale: interval
spacing between intervales is known but there is no absolute zero point
ex. temperature (C or F)
type of scale: ratio
spacing between intervals is known and a zero point exists
ex. time and distance
frequency distribution
how many times each score value is repeated via table or figure (bar graph)
histogram: shape
normal distribution bell curve - most scores cluster in the middle and then extreme scores at the tails
ex. IQ scores
histogram: symmetrical distributions
more than one peak - bimodal distribution
histogram: skewed distributions
the direction of skew is determined by which direction the tail of the distribution points
if the tail goes in a positive direction = positively skewed and vice versa for neg
histogram: central tendency
a typical or representative score value near the center of the distribution
variability
how different are the scores from one another
variable
a measurement that changes from one observation to the next
ex. CESD changes from one smoker to the next
constant
measurements that stay the same from one observation to the next
ex. boiling point of water
measures of central tendency: mean
numerical average, the sum of scores divided by sample size
∑x/n
measures of central tendency: median
the score value with half the scores above and below
50th percentile
organize from least to greatest and find middle
if odd number add together and divide by 2
measures of central tendency: mode
the most frequently occurring score
can be more than 1 in a given set
measures of central tendency: symmetrical distribution
unimodal distribution means that the three scores will be the same spot/number
measures of central tendency: skewed distributions
extreme scores have a larger effect on the mean
in these cases the median may be better
measures of variability: range
largest score minus smallest socre
doesn’t show the completely different distributions of values
very different sets could have the same range
measures of central tendency: sample variance
(s²) = sum of squared deviations of each score from the mean divided by n-1
variance determines the deviation around the mean
measures of central tendency: sample variance formula
s² = ∑(x - M)²/ n - 1
sum of (all values subtracted from the mean) squared/ divided by the number of cases minus 1
measures of central tendency: sample variance steps
- compute the mean
- compute each deviation
- square each deviation
- sum all the squares (sum of the square deviations from the mean)
measures of central tendency: standard deviations (s)
the square root of the variance
the measure of how dispersed the data is in relation to the mean
low deviation = the data is clustered around the mean
high deviation = the data is more spread out
validity
how well the measurement rule actually measures the variable under consideration as opposed to some other variable
ex. some intelligence test measures actual intelligence rather than being influenced by creativity or memory
reliability
an index of how consistently the rule assigns the same number to the same observation
ex. an intelligence test is reliable if it assigns the same number to an individual each time they take the text
cumulative frequency
the frequency of a score value plus the frequency of all smaller score values
ex.
Month # cars sold CF
January 20 20
February 30 20 + 30 = 50
March 15 50 + 15 = 65
April 18 65 + 18 = 83
relative frequency
divide the score value’s frequency by the total number of observations in the distribution
score value = 3
# of observations = 10
rf = 3/10
cumulative relative frequency
tabulation of the relative frequencies of all measurements at or below a given score value
class interval
range of score values
grouped frequency distribution
tabulation of the number of measurements in each class interval
relative frequency histogram
heights of bars represent relative frequencies of score values (or class intervals)
population variance
average of the squared deviations of each score from the mean (μ)
ex. {3,7,11}
sum of the square of distances = 32
32/3 = 10.6
population standard deviation (σ)
the positive square root of the population variance
