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Altius > Strategy, Math and Research Methods > Flashcards

Strategy, Math and Research Methods Flashcards

1
Q

How do you add numbers in scientific notation?

A

Make the exponents the same and then add/subtract the bases?

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2
Q

Multiply numbers in scientific notation

A

add the exponent; if same base add exponents, if different bases multiply the bases and raise to power

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3
Q

Divide two numbers in scientific notation

A

keep the base and subtract the exponents

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4
Q

Raise a number in scientific notation to a power

A

raise the base to the exponent and multiple the second portion by the exponent

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5
Q

Sin (0)=

A

0

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6
Q

Sin (30)=

A

1/2

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7
Q

Sin (45)=

A

sqrt 2/ (2)

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8
Q

Sin (60)

A

sqrt 3/ (2)

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9
Q

Sin (90)=

A

1

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10
Q

Cos (0)

A

1

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11
Q

Cos (30)`

A

sqrt 3/ (2)

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12
Q

Cos (45)

A

sqrt 2/ (2)

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13
Q

Cos (60)

A

1/2

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14
Q

Cos (90)

A

0

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15
Q

Trig Rules

A

SOH CAH TOA

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16
Q

Tan (theta)

A

sin (theta)/cos (theta)

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17
Q

sin^2x + cos^2x=

A

1

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18
Q

Area of a circle

A

pi r^2

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19
Q

Circumference of a circle

A

C=pi(d) or C=2pi(r)

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20
Q

Area of a triangle

A

1/2 bh

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21
Q

Volume of a sphere

A

V= 3/4 (pi)(r^3)

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22
Q

Pythagorean Theorem

A

A^2 +B^2 = C^2

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23
Q

All angles in a triangle must add up to

A

180 degrees

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24
Q

Converting degrees to radians RULES

A

pi radians= 180 degrees
2pi radians =360 degrees
approx. 6 radians in one circle
12 rad/s =2 rev/s

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25
Q

DECI- SI UNIT

A

1/10, 10^-1

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26
Q

CENTI- SI UNIT

A

10^-2

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27
Q

MILLI- SI UNIT

A

10^-3

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28
Q

MICRO-SI UNIT

A

10^-6

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29
Q

NANO-SI UNIT

A

10^-9

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30
Q

PICO- SI UNIT

A

10^-12

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31
Q

FEMTO-SI UNIT

A

10^-15

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32
Q

DECA-SI UNIT

A

10^1

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33
Q

HECTO-SI UNIT

A

10^2

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34
Q

KILLO-SI UNIT

A

10^3

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35
Q

MEGA-SI UNIT

A

10^6

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36
Q

GIGA-SI UNIT

A

10^9

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37
Q

TETRA-SI UNIT

A

10^12

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38
Q

Linear vs Non-linear graphs

A
  • if both contain squares/cubes graph will be linear

- if they are different vs variable –> graph will be non-linear

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39
Q

Steps for choosing a graph:

A

1) does y-value start high or low? (max/min values)
2) what is the slope and what does it represent?
3) what is the sign of the y-axis? is it always pos/neg? does it cross the x-axis?

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40
Q

Displacement vs. Time=

A

velocity

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41
Q

Velocity vs. time=

A

acceleration

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42
Q

Log- log graph

A

has a logarithmic scale on both axes

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43
Q

Semi-log graph

A

log scale on one axis, linear on the other

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44
Q

When to manipulate and equation:

A

1) If x doubles, what happens to y =?
2) 2 trials: predict change on a 2nd variable
3) data charts: look at chart and calculate factor
4) half in passage, half in question “volume doubled”
5) Ratio between A and B ***most difficult

45
Q

Peer review

A

research is submitted 1st, reviewed and critiqued by colleagues

46
Q

Verification

A

also known as replication, or the ability to reproduce results

47
Q

Experimental or Basic Science– RESEARCH

A

lab research under controlled conditions

48
Q

Human Subjects– RESEARCH

A

outside of lab on humans

ex: drug trials

49
Q

2 types of human subjects research

A
  • experimental: specific intervention given

- observational: observes data without direct control over variables or implementation of interventions

50
Q

Beneficence

A

do good; end study because of positive results that could potentially help other patients

51
Q

Non-maleficience

A

do no harm; end study because of negative results

52
Q

Types of Observation studies

A

Cohort, Cross-sectional, and Case-control

53
Q

Cohort Study

A

longitudinal study observing characteristics (risk factors) across time

54
Q

Cross-sectional study

A

analysis of data collected from population in a specific time

55
Q

Case-control study

A

observational study of individuals in population with condition present and compare to a control group without the disease (reference group)

56
Q

Independent Variable

A

the variable MANIPULATED or directly changed

“predictor variable”

57
Q

Dependent Variable

A

the variable MEASURED as a response to change

“outcome or effect” variable

58
Q

Control group

A

all conditions and environmental factors are IDENTICAL to the treatment group, but they are not receiving the treatment

59
Q

Positive Control

A

group is given treatment with a known outcome that can then be used to compare the the outcome of the treatment being studied

60
Q

Negative Control

A

group that does not receive any treatment; the known treatment has no effect or outcome
-ensures there is no response to the treatment

61
Q

Selection Bias (and types)

A 
selection of participants is not truly random and do not represent the population accurately 
types:
-specific real area
-Self-selection
-prescreening/advertising
-exclusion bias
-healthy-user
-Berkson's fallacy 
-Overmatching
62
Q

Specific real area bias

A

bias introduced by conducting study in a specific area that does include a representative sample

63
Q

Self-selection bias

A

introduced when a participant in study has the choice to participate or not, or to determine level of involvement

64
Q

Prescreening/advertising Bias

A

occurs when screening or advertising process results in an unrepresentative sample

65
Q

Exclusion Bias

A

to the exclusion of an entire group from a population

66
Q

Healthy-user Bias

A

persons included in the study are healthier than general population
ex: choosing triathlon runners for study of CV disease

67
Q

Berkson’s Fallacy

A

participants are less healthy than the general population

ex: choosing hospital patients

68
Q

Overmatching

A

negative outcome resulting from what is normally good practice; matching for potentially confounding variables

69
Q

Observer Bias

A

observers or researchers know the goals of the study or hypotheses and allow this knowledge to influence their observations during the study

70
Q

Demand Characteristics

A

participants form an interpretation of experiments purpose and unconsciously change their behavior to fit this model

71
Q

Information Bias

A

wrong or inexact recording of variables or data

  • continuous variable (blood pressure): called measurement error
  • categorical variable (disease state): called misclassification
72
Q

Confounding Variable

A

extraneous variable that influences variables being studied, but is not a part of the expected correlation

73
Q

Placebo effect

A

example of a confounding variable; participants are given a placebo and experience real or perceived health benefits as a result of their belief that they are being treated

74
Q

Detection Bias

A

differences between groups caused by inconsistencies in the method of detection or diagnosis

75
Q

Performance Bias

A

difference between groups in terms of care receives or treatment

76
Q

Experimenter Bias

A

errors introduced to expectations of investigator
types:
-confirmation bias
-reporting bias

77
Q

Conformation Bias

A

tendency to favor info that confirms ones hypothesis and dismiss others that go against it

78
Q

Reporting Bias

A

some findings are reported others are not; withholding or ignoring data

79
Q

Accuracy

A

difference between measurement and the actual value

80
Q

Precision

A

variation in measurement seen when recording

81
Q

Reliability

A

consistent and repeatable results

82
Q

Test-Retest Reliability

A

consistent and repeatable results

83
Q

Inter-Rater reliability

A

extent to which two or more raters (observers) agree

84
Q

Validity

A

test or experiment tests what it is supposed to measure and uses methods that meet scientific standards; internal and external validity

85
Q

Internal Validity

A

extent to which study findings of truth or causation are justifiable; the scientific rigor of the study

86
Q

External Validity

A

generalizability; degree to which findings can be extrapolated to general population

87
Q

Correlation Coefficient (r^2)

A
  • Linear regresson: correlation coefficient related to linear regression analysis
  • r^2 is a measure of how tightly the data fits the line– closer to the data, as a whole, the higher the r^2 value
  • r^2=1 is a perfect fit
88
Q

Hill’s Criteria is used for what?

A

whether or not a causal relationship exists

89
Q

Hills Criteria and definitions

A
  • Specificity: single cause produces a single effect
  • Plausability: reasonable path to link outcome to exposure
  • Dose-response: increase amount of exposure, increase the risk
  • Testable by experiment: condition can be altered with different treatments
  • Coherence: association should be compatible with existing theory and knowledge, including knowledge of past causes and epid. studies
  • Analogy: similar association are known to exist that are analogous to associations being made
90
Q

Single-bling experiments

A

subjects are blinded to the purpose, treatment and control group

91
Q

Double-blind experiments

A

experimenters and subjects are blinded to the purpose, treatment, and control

92
Q

Sample

A

portion of a population that is included in the data

93
Q

Population

A

all members in the group or category being sampled (ex: all US veterans)

94
Q

Statistic

A

measure or data point that is calculated for the sample (average income among veterans)

95
Q

Parameter

A

a measure that is calculated for the entire population, not just the sample

96
Q

Mean, median, mode

A

Mean- the average value
median- middle of the data
mode- most frequently occurring data point
range- the minimum data point to the maximum data point

97
Q

Standard Deviations

A

how tightly associated the data are to the mean; small means narrow set of data, close to the mean. Large SD indicated greater spread of the data around the mean

98
Q

Standard Deviations

A

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

99
Q

Probability RULES

A

o Assumption = Outcomes are independent (i.e., do NOT influence one another) and are also mutually
exclusive (i.e., they cannot occur together). o AND vs. OR
▪ AND = MULTIPLY the probabilities of individual events to get the overall probability of all events occurring.
▪ OR=ADDtheprobabilitiesofeachindividualeventtogethertogettheoverallprobabilityofANY of the events occurring.

100
Q

Null Hypothesis

A

H0 is the LACK OF A RELATIONSHIP OR GROUP DIFFERENCE; no statistically significant differences between groups– no relationship

101
Q

Alternative Hypothesis

A

opposite of the Null; there is PRESENCE OF A RELATIONSHIP OR GROUP DIFFERENCE; there is a difference between groups— there is a relationship

102
Q

T or Z test –> P value

A
  • test statistic is calculated
  • result is compared to a table of t-values or z-values
  • table indicated the significance level
103
Q

Significance Level (alpha)

A

alpha=0.05; 0.01, 0.001
p< 0.05 means that we can be 95% confident that the results are real
p<0.01 means we can be 99% confident

104
Q

If hypothesis is directional (A is larger than B)

A
  • if p> alpha CANNOT reject H0=NO STATISTICAL significance

- of p< 0.05 we REJECT H0=STATISTICALLY significant

105
Q

If hypothesis is non-directional (there is a difference between A and B. no assertion made as to whether it is larger or smaller)

A
  • If p> alpha/2 we CANNOT reject H0=NO STATISTICAL SIGNIFICANCE
  • if p< alpha/2 REJECT H0-STATISTICALLY SIGNIFICANT
106
Q

Type 1 errors

A

researchers claimed a difference between groups when none existed (they rejected H0 but should not have)
“false positive”

107
Q

Type 2 errors

A

researchers did not claim a difference between groups when one DID exist
aka “false negative”
(should have rejected H0 but did not)

108
Q

Confidence Interval

A

95% is standard
(z score)(SD)= error margin
C.I.= Mean +/- Error margin

109
Q

Standard Error of the Mean (SEM)

A

a quantification of how precisely the mean represents the true mean of the population; SEM decreases as sample size increases

Altius (5 decks)

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