- data will be collected on desired variables - dependent variables= outcome variables - independent variables - comparisons= statistical analyses - inferences= made about measurements and their comparisons in relation to the null (also be made about full population of similar subjects= generalizability)

- researchers either accept or reject based on data analysis - states there will be no true difference between groups being compared - most conserved/commonly used - perspectives: superiority, non-inferiority, equivalency

- dimensionality - bigger/taller/shorter/stronger - does the data have magnitude?

2. Consistency of Scale

- fixed interval - equal, measurable spacing between units - height= inches, feet, cm - does the measurement being acquired have consistency of scale?

- dichotomous/binary - non-ranked, named categories - no magnitude/ no consistency of scale/ no rational zero - labeled variables w/o quantitative characteristics - all data pushed into 2 categories - ex: gender? hair color? occupation?

- ranked categories, non-equal distance - yes magnitude/ no consistency of scale/ no rational zero - all pain scales (even numbers are ordinal because numbers are place holders)

- order and magnitude and equal intervals of scale (units) - yes magnitude/ yes consistency of scale - no rational zero= interval - yes rational zero= ratio ex) number of living siblings, personal age, blood sugar, labs

Intro to Biostats lecture 29-34 Flashcards by brittney acre

Study Subjects:

Sample

a subset or portion of the full population “representatives”
useful when studying the complete population is not feasible
random processes commonly utilized to draw a sample
hope that info generated from sample is generalizable to entire population

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Study Measurements

data will be collected on desired variables
dependent variables= outcome variables
independent variables
comparisons= statistical analyses
inferences= made about measurements and their comparisons in relation to the null (also be made about full population of similar subjects= generalizability)

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Null Hypothesis (Ho)

researchers either accept or reject based on data analysis
states there will be no true difference between groups being compared
most conserved/commonly used
perspectives: superiority, non-inferiority, equivalency

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Alternative Hypothesis (H1)

-a research perspective which states there will be a true difference between the groups being compared

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3 attributes of data measurement:

magnitude
consistency of scale
rational/absolute zero

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Magnitude

dimensionality
bigger/taller/shorter/stronger
does the data have magnitude?

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Consistency of Scale

fixed interval
equal, measurable spacing between units
height= inches, feet, cm
does the measurement being acquired have consistency of scale?

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3 categories for data based on the 2 key attributes:

nominal
ordinal
interval/ratio

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Nominal

dichotomous/binary
non-ranked, named categories
no magnitude/ no consistency of scale/ no rational zero
labeled variables w/o quantitative characteristics
all data pushed into 2 categories
ex: gender? hair color? occupation?

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Study Subjects:

Population

all individuals

- not to be confused with “study population”

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Ordinal

ranked categories, non-equal distance
yes magnitude/ no consistency of scale/ no rational zero
all pain scales (even numbers are ordinal because numbers are place holders)

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Interval/Ratio

order and magnitude and equal intervals of scale (units)
yes magnitude/ yes consistency of scale
no rational zero= interval
yes rational zero= ratio

ex) number of living siblings, personal age, blood sugar, labs

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how can you change levels of measurement data?

-after data is collected we can appropriately go down in specificity/detail of data measurement but never go up!
ratio>interval>ordinal>nominal
*can make interval ordinal/nominal but not vice versa

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what are descrete variables?

categorical in nature

- nominal and ordinal

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what are continuous variables?

have scale

- interval/ratio

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what are the measures of central tendency

mean/median/mode
min/max/range
interquartile range
mode useful for nominal/ordinal/interval data
mean/median/ range useful for interval only

what is variance and standard deviation?

variance= differences in each individual measurement value and the groups mean
interpretation of the spread of the data, tells us how uniform or not the data is

-SD= square root of variance value (restores units of mean)

Normally Distributed data

symmetrical shape
parametric tests most useful
mean/median/mode are equal or near equal
equal dispersion of curve tails to both sides of the mean/med/mode

what is the % of the population compared in 1, 2, 3 standard deviations?

1 SD= 68%
2 SD= 95%
3 SD= 99%

Positively Skewed data

asymmetrical w/ tail pointing to the right
skewed anytime the median differs from the mean
when the mean higher than the median

Negatively Skewed data

asymmetrical distribution with one tail pointing toward the left
distribution is skewed anytime the median differs from the mean
when the mean is lower than the median

what is skewness?

a measure of the asymmetry of a distribution
perfectly normal distribution is symmetric and has a skewness value of 0
number closest to 0 is normally distributed and as you move away from 0 (3,4,8,20) seeing more skewness

what is kurtosis?

a measure of the extent to which observations cluster around the mean
normal distribution the value is 0 (near 0)
positive kurtosis= more cluster
negative kurtosis= less cluster

*discrete data usually has positive kurtosis because more cluster around

what are required assumptions of interval data?

for proper selection of a parametric test data must be
1. normally distributed
2. equal variances
3. randomly derived and independent

how can we tell if variances are equal?

- use levene's test | - in SPSS test to see if variances are equal

how do we handle interval data that is not normally distributed?

1. use a statistical test that does not require the data to be normally distributed (non parametric tests)- treat as if ordinal or nominal 2. transform data to a standardized value (z-score or log) and hope that makes it normally distributed **always run descriptive stats and graphs to see if normal distribution

what is Power?

1-beta - ability of the study design and selected stats test to detect a true difference if one truly exists between group comparisons and therefore is the level of accuracy in correctly accepting/rejecting the null - ability to find the difference if there is one

how does sample size affect power?

- the large the sample size the greater the likelihood of detecting a difference if one truly exists * increase sample size= increase power

what determines sample size?

1. minimum difference between groups deemed significant - smaller the difference between groups necessary to be significant the greater the number needed 2. expected variation of measurement (known or estimated) 3. alpha and beta error rates (power)

Null Hypothesis (H0)

- a research perspective which states the will be no true difference between the groups being compared - most conservative and commonly utilized - can take various perspectives (superior, noninferiority, equivalency)

Alternative Hypothesis (H1)

-a research perspective which states there will be a true difference between the groups being compared

different ways to explain statistical significance | P value interpretation

- the probability of making a type 1 error if the null is rejected - the probability of erroneously claiming a difference between groups when one does not really exist - the probability of the outcome of the groups differences occurring by chance