Exam 1 Flashcards
What are the 4 methods of establishing truth?
Authority, Rationalism, Intuition, and Scientific Method
Authority
accepting information because it is from a highly respected source
Rationalism
acquisition of knowledge through reason or logic
Intuition
common-sense approach to acquiring knowledge; not based on reason
Scientific Method
relies on objective assessment
Observation vs Experimental
Observation does not influence any variable while experimental influences variables
Descriptive vs Inferential Statistics
Descriptive describes the data while inferential is used to make inferences (generalization)
Population
All the individuals of interest in a study
Sample
A set of individuals selected from a population
Variable
something that changes or has different values for different individuals
Independent Variable
is the variable that is manipulated
Dependent Variable
variable that is observed to asses the effect
Constant
value does not change; control
Data
measurement of observation of a variable
Statistic
a value that describes a sample
Parameter
value that describes a population
Why is random sampling important?
It allows the data to be representative of the population
What are the four measurement scales?
Nominal, Ordinal, Interval, and Ratio
Nominal
label and categorize, NO NUMBERS; think of brands
Ordinal
categorizes observations by ORDER; think of 1st place
Interval
ordered categories, interval between categories of equal size; NO ZERO POINT (think of temperature)
Ratio
same as interval but HAS A ZERO POINT (absolute zero); think of height
Discrete Variable
whole countable numbers
Ex: 1, 2, 3, 4…
Continuous Variable
infinite numbers possible
Ex: 1, 1.25, 1.26, 2, 2.34…
Real limits
upper limit: will be .5 above the highest number
lower limit: .5 below the lowest number
EX: interval is 1-5 -> real limit is .5-5.5
Rounding Numbers
below .5 = keep the same
above .5 = round up
equal to .5 (even) = stays the same
equal to .5 (odd) = round to next even number
Frequency Distribution, why is it useful?
Present score values and their frequency of occurrence
shows the entire data
Ungrouped vs. Grouped Frequency Distribution
ungrouped: raw scores can be pulled out, can find individual score
grouped: raw scores cannot be pulled out, no individual score, information is lost
how to find the interval
i = range/ # of class interval
* class intervals are given*
Relative Frequency (relative f)
proportion of total in interval
relative f = f/N
* should add up to 1*
Cumulative Frequency
number of scores that fall bellow the upper real limit
* start at the bottom then add up
* final answer should equal to N
Cumulative Percentage
Percentage of scores that fall below upper real limit of each interval
Cumulative % f= (f/N) x 100
Percentile Point
% that falls bellow a specifice percentage (P30 = 30% of scores fall below this point)
Percentile Point Formula
XsubL + (i/fsubi)(cumfsubp - cumfsubL)
XsubL = lower real limit of interval containing the percentile point
i = interval width
fsubi = freq. within the interval
cumfsubp = N x deciimal form of Psubx
cumfsubL = freq scores below the lower real limit (USE THE ROW BELOW)
Percentile Rank
percentage of scores with values lower than the score in question
ex: 10% of scores fall below 59
Percentile Rank Formula
(cumfsubL + (fsubi/i)(X-XsubL) / N) x 100
Bar Graphs
best used for nominal or ordinal data
*bars do not touch
Histogram
Best for interval or ratio data
* bars do touch each other
Frequency Polygon
Used for interval or ratio data
* point is plotted over the midpoint of each interval
Symmetrical vs Positive Skewed vs Negative Skewed
Symmetrical: normal curve
Positive: tail on the right
Negative: tail of the left
Mean vs Median vs Mode
Mean: sum of all scores divided by total number
* is affected by outliers
Median: midpoint score
* not influenced by outliers
Mode: most common observation
* can be used for nominal data
Overall Mean formula
M= sigmaX1 + sigmaX2 / n1 + n2
sigmaX1 = X-bar x n
Mean and Median in Skewed Dist.
Positive = mean > median
Negative = mean < median
Deviation Score
tells how far away the raw score is from the mean
sample: X - X-bar
Population: X - mu
Sum of Squares (SS)
Raw Score method: sigmaXsqr - (sigmaX)sqr/N
Variance calc. from SS
population: SS/N
sample: SS/N-1
Standard Deviation calc. from SS
Population: sqrt SS/N or sqrt variance
Sample: sqrt SS/n-1 or sqrt variance
Three measures of Variability
Range: max - min; overall spread
* is affected by extreme scores
Variance: average distance from the mean in squared units
Standard Deviation: average distance from the mean
Z-Score
how many SD our score is from the mean
