# Strategy & Math Flashcards Preview

## ALTIUS Cards > Strategy & Math > Flashcards

Flashcards in Strategy & Math Deck (166)
1
Q

cos 0 30 45 60 90

A
2
Q

sin 0 30 45 60 90

A

1

3
Q
• SI units:
• Femto
• Kilo
• Deci
A

Femto

• 10-15

Kilo

• 103

Deci

• 10-1
4
Q

SI Units:

• Mega
• Pico
• Hecto
A

106

10-12

102

5
Q

SI Units:

• Tera
• Nano
• Centi
A

1012

10-9

10-2

6
Q

SI Units:

• Deca
• Giga
• Micro
• Milli
A

101

109

10-6

10-3

7
Q

Convert 650 nm to SI units

A

650 x 109

8
Q

Const. force (not velocity!) causes WHAT acceleration?

A

constant acceleration

9
Q

When you throw a baseball, when is the only time it is accelerating?

A

Only when it is in contact with your hand

10
Q

Increasing something by 25% is the same as multiplying it by what?

A

x 5/4

11
Q

Define “mass”

A

the measure of an object’s inertia

12
Q

Define “inertia”

A
• the ability of an object to RESIST its change in velocity
13
Q

Where on a mass/object is its “Center of Gravity?”

A

is at the center of the mass/object

14
Q

Where is the “Center of buoyancy?”

A
• at center of mass of the FLUID displaced by the submerged object
15
Q

Scalar or Vector?

Mass

A

scalar

16
Q

Scalar or Vector?

temperature

A

scalar

17
Q

Scalar or Vector?

velocity

A

vector

18
Q

Scalar or Vector?

speed

A

scalar

19
Q

Scalar or Vector?

displacement

A

vector

20
Q

Scalar or Vector?

acceleration

A

vector

21
Q

Scalar or Vector?

force

A

vector

22
Q

Scalar or Vector?

work

A

scalar

23
Q

Scalar or Vector?

energy

A

scalar

24
Q

Scalar or Vector?

weight

A

vector

25
Q

Scalar or Vector?

charge

A

scalar

26
Q

Scalar or Vector?

electric field

A

vector

27
Q

Scalar or Vector?

magnetic field, B

A

vector

28
Q

Scalar or Vector?

time

A

scalar

29
Q

Scalar or Vector?

momentum

A

vector

30
Q

Scalar or Vector?

impulse

A

vector

31
Q

Scalar or Vector?

density

A

scalar

32
Q

Scalar or Vector?

torque

A

vector

33
Q

What are the 4 questions that test conceptual understanding?

A
1. Can I visualize it?
2. Can I draw a picture/graph/diagram of it?
3. Can i explain it to someone in layman’s terms?
4. Can i think of and describe real-life examples?
34
Q
• Area of triangle
• formula=?
A

Atri=1/2 bh

35
Q

Vol of sphere formula

A

Vsphere=4/3 πr3

36
Q

SA of sphere formula

A

4pir^2

37
Q

Manipulating equations mnemonics

A

SSISDDODIOSD

38
Q

sqrt 2=

A

1.4

39
Q

sqrt 3=

A

1.7

40
Q

sqrt 2/2=

A

.7

41
Q

sqrt 3/2=

A

.9

42
Q

product of [H][OH] always equals:

A

1x10^-14

43
Q

Doppler effect formula

A

deltaf/fs=v/c

44
Q

Be careful of S.N.E.W.L

A

Qualifiers (Write these down!) “StrengthensNot ExceptWeakensLeast”

45
Q

tan0=

A

sin0/cos0

46
Q

sin^2x+cos^2x=

A

1

47
Q

11^2

A

121

48
Q

12^2

A

144

49
Q

13^2

A

169

50
Q

14^2

A

196

51
Q

15^2

A

225

52
Q

Multiplying 2 vectors: If answer is scalar, (ie work), also mult by what?

A

cos0

53
Q

multiplying 2 vectors: if answer is a 3rd vector (ie torque), also multiply by what?

A

sin0T=Frsin0

54
Q

Decimal equivalent:1/5

A

.2

55
Q

Decimal equivalent:1/8

A

.125

56
Q

Decimal equivalent:1/9

A

.11

57
Q

For fractions where numerator is higher (like 13/5), what should you do to solve?

A

Create compound fraction: 5x2=10, left w/ 3/5 13/5 becomes 2x (3/5)

58
Q

For fractions where denominator is larger, what should you do to solve?

A

“High/Low” methodChange denominator to 1 digit higher or 1 digit lowerex:3/7 changed to 3/6 and 3/8. Must be a little less than .5

59
Q

When multiplying with scientific notation, what happens to the exponents?

A

60
Q

When dividing with scientific notation, what do you do with the exponents?

A

You subtract them

61
Q
• Estimating Fractions

29/4 =?

A

=7.25

4 x 7=28

¼ is left behind

62
Q
• Estimating Square Roots
• “High/Low” Method

What is the square root of 72?

A
• √81=9
• 64=8

∴ the square root of 72 is somewhere in between, so about 8.5

63
Q
• Surface Area of a Sphere
• Formula=?
A

SAsphere= 4πr2

64
Q
• Trigonometry
• All of the angles in any triangle must add up to?
A

180°

65
Q

Trigonomic Relationships

• sinθ=
• cosθ=
• tanθ=
A

sinθ=Opp/Hyp

​”SOHCAHTOA”

66
Q

• How many radians are in ONE CIRCLE?
• ∴, if something is turning at 12 rad/sec, it is making approximately ___ revolutions/sec
A

There are approximately 6 radians in ONE CIRCLE

• ∴, if something is rotating at 12 rad/sec, it is making 2 revolutions/ sec
67
Q

Trigonomic Relations

• What are the INVERSES of sin, cos, and tan?
A
• sin-1
• cosecant
• cos-1
• secant
• tan-1
• cotangent
68
Q

Trigonomic Relationships

• tanθ= ?
A

tanθ=sinθ / cosθ

69
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y=x

A
70
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = 1/x

A
71
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y=x2

A
72
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = |x|

(Absolute Value)

A
73
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = x3

<em><span>(Cubic)</span></em>

A
74
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = √x

A
75
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y= 3√x

(Cube Root)

A
76
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = ln x

(Logarithmic)

How does it look different than y= logx?

A
77
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = sinx

A
78
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = cosx

A
79
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = ax

<span>(<em>Exponential)</em></span>

A
80
Q

Linear & Non-Linear Graphs

• What does a graph look like for:

y = 1/x

(Reciprocal)

A
81
Q

Linear & Non-Linear Graphs

• For the equation X= ½at2
• Which of the following relationships will be LINEAR?
• Which will be NON-LINEAR?
1. ​X vs. t (or t vs. X)
2. X vs. a (or a vs. X)
3. a vs. t (or t vs. a)
A

X=½at2

1. ​NON-linear
2. Linear
3. NON-linear

82
Q

Linear & Non-Linear Graphs

What does a graph look like for:

y = tan x

A
83
Q

• If a variable is changing exponentially, will be linear or non-linear on:
• a semi-log graph?
• a log-log graph?
A

The log of an exponential function becomes linear, so exponential functions graphed on semi-log axes end up being linear

• ​…AND a linear function would be non-linear

If the exponential function is graphed on log-log axes, the function will be non-linear

• The table below may be helpful*
• The semilog graph described is for a linear X-axis and a logarithmic Y-axis (log-lin type not lin-log)
84
Q

• Describe:
• a semi-log graph
A
• A semi-log graph has a logarithmic scale on one axis, but a linear scale on the other axis
85
Q

• Describe:
• a log-log graph
A

A log-log graph has a logarithmic scale on BOTH axes

86
Q

Manipulating Equations

ay=vx2/cq

• How are a and y related to each other?
A

inversely

87
Q

Manipulating Equations

ay=vx2/cq

How are x and q related to each other?

A

directly

88
Q

Manipulating Equations

ay=vx2/cq

How are a and q related to each other?

A

directly

89
Q

Manipulating Equations

ay=vx2/cq

How are a and c related to each other?

A

inversely

90
Q

Manipulating Equations

X=½at2

What will happen to time if the distance (x) is tripled?

A

x∝t2

• If the distance traveled (x) increases by a factor of three (3), then time must increase by some factor that, when squared, will also equal a factor of three
• This number is the square root of three, so time will increase by a factor of 1.7—*
• or be 1.7 TIMES larger than it was originally*
91
Q

√3=

A

1.7

92
Q

Well-designed research must have a hypothesis that is….?

A

a Testable hypothesis

• basically, it can be used to verify a clear YES or NO answer
93
Q

Types of Research

• Decribe Experimental or “Basic Science” Research
• What kind of environment is it conducted in?
• Is it conducted on Human subjects?
• What does this type of research allow investigators to have?
• The main goal of this type of research is to indicate…?
A

Experimental or “Basic Science” Research

• Laboratory research conducted in a highly controlled environment;
• NOT on human subjects!
• This type of research allows investigators to have the strictest level of control over all possible variables and conditions
• Experimental research is therefore thought to be the most reliable way to indicate CAUSATION
94
Q

Types of Research

• Human Subjects Research
• Where is it conducted?
• What is a common example of this type of research?
• How does it compare (in efficiency and level of control) to “Basic Science” research?
• Are its conclusions more or less definitive?

What are the 2 Types of Human Subjects Research?

A

Human Subjects Research

2 Types: Experimental & Observational

• Research conducted outside the laboratory, often on human subjects
• Drug trials are a common and familiar example
• Has less control over conditions than in “Basic Science” research*
• making conclusions less definitive*
• For example, a study drug may fail to show decreased blood pressure in some subjects
• However, it is later discovered that all participants did not follow the strict low-sodium diet required by the study guidelines
• This was thought to influence blood pressure measurements in those patients
95
Q

Human Subjects Research

• Describe “Experimental” Research
• Research involves a specific ________ controlled by the ________.
• Subjects are separated into _____ and ________ groups
A
• Research involves a specific intervention controlled by the investigator
• Subjects are separated into control and treatment groups.
• e.g., To test the efficacy of a new drug, patients with allergies are separated into groups randomly and given either the drug (treatment) or placebo (control)
96
Q

Human Subjects Research

• Describe “Observational” Research
• Investigator _______ data WITHOUT….? (2)
A
• Investigator observes data WITHOUT:
• Direct control over the variables, OR
• Implementation of interventions

Example:

An investigator reviews case studies from COPD [chronic obstructive pulmonary disease] patients and examines demographic information and lifestyle choices in an attempt to identify risk factors associated with COPD)

97
Q

Medical Ethics

• Describe (in general) “Beneficence
• One important aspect of this is ending a study because of…?
A

DO GOOD.

• Doctors and researchers have an obligation to promote the welfare of patients or study participants
• Patient welfare should always be a primary consideration in study design and execution

ENDING A STUDY BECAUSE OF POSITIVE (“GOOD”) RESULTS:

• One classical application of beneficence in human subjects research is the obligation to END an experimental study when it is clear a drug or intervention results in obvious benefit
• This may sound counterintuitive, but remember that each study must have control groups
• If a drug is found to save the lives of dying cancer patients…..
• it is NOT ethical to continue the study long-term and thereby save the lives of those in the treatment groups
• …while those in the control groups are withheld from taking a drug researchers know could help them

98
Q

Medical Ethics

• Describe (in general) “Nonmaleficence
• One important aspect of this is ending ​a study because of…?
A

DO NO HARM

• This is the physician’s oath, but applies equally to researchers
• Doctors and researchers have an obligation to not harm their patients or study participants

ENDING A STUDY BECAUSE OF NEGATIVE (“BAD”) RESULTS:

• Researchers are similarly obligated to end a research study as soon as it is verified that a treatment harms the subjects
• Most early research that is criticized today is drawn into question because it violated this principle
• The famous psychology study involving “Little Albert,” for example, while revealing evidence about conditioning, is now thought to have had an unethical impact on the young child involved :(
• Albert was conditioned to have severe generalized phobias of animals and the study involved obvious emotional trauma to Albert.

99
Q

Medical Ethics

• Describe (in general) “Autonomy
A

Patient autonomy and informed consent

• Physicians and researchers have an obligation to:
• Inform patients or study participants, and
• Allow them to make decisions about their own health and treatment

At times, some deception (e.g., placebo) is necessary to effect research

• However, this should be the minimal amount possible, the truth should be revealed as soon as possible, and deception in general should be approved by an Internal Review Board (IRB)
100
Q

Medical Ethics

Describe (in general) “Justice

• Hint: “Equal…”
A

Equal treatment of all people

• Equal allocation of resources, to the extent possible
• ..without bias, prejudice or discrimination.
101
Q

Observational Research Study Types

• Describe a “Cohort Study
• Cohort Studies usually employ _______s to demonstrate a ______
• Give an example

Hint:

A “cohort” is an ancient Roman military unit, comprising six centuries, equal to one tenth of a legion

THINK: Cohort = GROUP

A

Cohort Study

• is a longitudinal study observing characteristics (usually risk factors) of members of a cohort across time

Cohort studies usually employ correlations to demonstrate a relationship

EXAMPLES:

• Smokers (Cohort A) were three times more likely to develop lung cancer before the age of 50 than non-smokers (Cohort B)
• A statistically significant correlation (r = 0.84, p < 0.01) exists between socioeconomic status and frequency of pre-term births

Notice that this is NOT the manipulation of an independent variable, THEN observing outcomes

• Scientists are simply observing data about people or populations as it exists and looking for relationships
102
Q

Observational Research Study Types

• Describe a “Cross-Sectional Study”
• Give some examples
A

Cross-Sectional study

• is the analysis of data collected from a population or sample AT ONE SPECIFIC TIME
• …compared to across a time period for cohort studies

EXAMPLES:

• A survey of the U.S. population to determine the current prevalence of a disease
• A study examines blood pressure among those with incomes above \$100K/year and those below \$100K.
103
Q

Observational Research Study Types

• Describe a “Case-Control Study”
• By design, a Case-Control Study is always ________
• Give an example
A

Case-Control Study

By design, a Case-Control Study is always RETROSPECTIVE

• Is an observational study of individuals in the population WITH a condition present
• …and comparison of that group to a control group of persons WITHOUT the disease IN THAT SAME POPULATION (i.e., “reference group”)

Most famous Case-Control Study is the one is the study that linked lung cancer to smoking

EXAMPLE:

• There is a suspicion that zinc oxide (the white non-absorbent sunscreen traditionally worn by lifeguards) is more effective at preventing sunburns that lead to skin cancer than absorbent sunscreen lotions
• A case-control study was conducted to investigate if exposure to zinc oxide is a more effective skin cancer prevention measure
• The study involved comparing a group of former lifeguards that HAD developed cancer on their cheeks and noses (cases) to a group of lifeguards (same population) WITHOUT this type of cancer (controls)
• …and assess their prior exposure to:
• zinc oxide OR
• absorbent sunscreen lotions
• This study would be retrospective in that the former lifeguards would be asked to recall (“Recall Bias”) which type of sunscreen they used on their face and approximately how often
104
Q

Observational Research Study Types

• What are some Pros & Cons of:
• Cross-Sectional Studies
A
105
Q

Observational Research Study Types

• What are some Pros & Cons of:
• COHORT Studies
A
106
Q

Observational Research Study Types

• What are some Pros & Cons of:
• Case-Control Studies

Also:

What kind of outcome are these studies useful for?

A

Useful for RARE outcomes

107
Q

Observational Research Study Types

• Differentiate b/t:

Prospective & Retrospective Cohort Studies

• What’s going on wrt the Exposure & Outcome?
A
108
Q

Independent vs. Dependent variables

• Differentiate b/t the two
• For each:
• “Also called the ____ variable”
• “It can be thought of as the….?”
• What axis of a graph does each go on?

HINT: “DRYMIX” or “I’M a DR.

A

INDEPENDENT VARIABLE

• The variable that gets:
• MANIPULATED (or rather…)

changed BY THE INVESTIGATOR!!

• Also called the “predictor variable.”

It can be thought of as the “CAUSE

• Always goes on the X-axis

DEPENDENT VARIABLE

• The variable MEASURED
• ….as a response to changes in the INdependent variable
• Also called the “outcome variable

It can be thought of as the “EFFECT

• You can’t “Add” a Dependent Variable
• i.e., you can “add” an outcome…you can only “add” something that will change the outcome
• Always goes on the Y-axis
109
Q

Independent vs. Dependent variables

• Identify the independent and dependent variables in the following scenarios:
1. Time spent studying and test score
2. Gas mileage and octane rating of the gas used
3. Dosage of medication used and lab rat survival rate
4. Level of aggression and amount of exposure to violent video games
A
1. Independent: Time Spent studying
• Dependent: Test score
2. Independent: Octane rating of gas
• Dependent: Gas mileage
3. Independent: Dosage of medication
• Dependent: Survival Rate
4. Independent: Exposure to violent VG’s
• Dependent: Lvl of aggression
110
Q

Study Methods

• Define a “Control Group
A

Control Group

A group or trial in which ALL conditions and environmental factors are IDENTICAL to the treatment group–

EXCEPT for the treatment itself!!

111
Q

Control Groups

• Define a “POSITIVE Control”
• What purpose do they serve in an experimental study?
• An experiment results in the ability of bacteria to grow on a petri plate containing antibiotic (POSITIVE control=?)
A

POSITIVE control

• Is a group given a treatment with a KNOWN OUTCOME

PURPOSE:

• The positive control can be compared to the unknown outcome of whatever treatment is being studied

EXAMPLE:

• If your experiment results in the ability of bacteria to grow on a petri plate containing antibiotic, your positive control will be bacteria that are known to carry the appropriate drug resistance marker

Even if none of your experimental bacteria grow, as long as there is growth of the positive control

• ….you know that growth was at least POSSIBLE
112
Q

Control Groups

• Define a “NEGATIVE Control”
• What purpose do they serve in an experimental study?
• HINT: What can they help expose?
• An experiment results in the ability of bacteria to grow on a petri plate containing antibiotic (NEGATIVE control=?)
A

NEGATIVE control

• is a group that does NOT receive any condition or treatment
• …and for which NO outcome is expected

PURPOSE:

• To help expose CONFOUNDING variables

EXAMPLE:

• An experiment results in the ability of bacteria to grow on a petri plate containing antibiotic

NEGATIVE Control:

• Bacteria which do NOT carry a drug resistance marker should NOT be able to grow on a petri plate containing antibiotic

∴ if growth IS observed, it is a red flag that something is WRONG with the experiment!

• “What could be one reason for growth?”
• ​aka what could be a confounding variable here?
113
Q

Evaluating Research

• Sources of Experimental Bias or Error
• Define “Selection Bias”
A

Selection Bias

The method used to select participants is

NOT TRULY RANDOM

• the results are not representative of the whole population
• There are “systematic BASELINE differences” among participants

….because true randomization was not achieved

114
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Specific Real Area Bias”

• Give an example
• Will having a specific location for a study always mean this type of bias will exist?
• Give an example why/why not
A

SPECIFIC REAL AREA BIAS:

• is bias introduced by conducting the study in a specific area that does NOT include a representative sampling of the population being studied

For example:

• A study of U.S. eating habits (population= U.S. citizens) conducted at the gym leaves out all those who do not attend the gym—particularly bad because:
• being a member of a gym is likely to confound with eating habits

Having a specific location for a study does NOT always mean “specific real area bias” will exist!

For example:

• a study investigating the drug-using habits of Maricopa County High School students (population=HS students) would not be biased if conducted only at the high school
• because the sample would be representative of the population
115
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Self-Selection Bias”

• What type of research shows SIGNIFICANT self-selection bias?
• Give an example of SSB
A

Self-Selection bias

• is bias introduced when participants in the study have the ability to:
• choose to participate or not to participate, OR ,
• to determine their level of involvement

Surveys show significant self-selection bias

For example:

• Feedback boxes at fast-food establishments are far from representative of customer opinions
• Generally because those with negative opinions have been shown to be far more likely to fill out such surveys
• (as opposed to those with positive experiences)
116
Q

Evaluating Research

• Sources of Experimental Bias or Error

• Give an example wrt advertisements looking for volunteers for a study being done on obesity
A

• Occurs when the screening or advertising *PROCESS* *ITSELF* results in
• …an unrepresentative sample

For example:

• “Volunteers needed for an obesity study”

OR

• “Volunteers needed for a weightloss study”

will likely elicit different volunteers

117
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Exclusion Bias”

• Give an example
A

Exclusion Bias

• Refers to the exclusion of an entire group from the population

Example:

• A study about childhood education that does not survey any homeschooled children
118
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Healthy User Bias”

• Give an example
A

Healthy User Bias

• When the persons included in the study are likely to be healthier
• ….than the general population

Example:

• Studying cardiac atherosclerosis among participants in a triathalon
119
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Berkson’s Fallacy

• How does it compare to Healthy User Bias?
A

Berkson’s Fallacy

• Is the selection of participants from hospitals
• where the participants are likely to be LESS healthy than the general population

Is the OPPOSITE EFFECT of the Healthy-User Bias

120
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Overmatching

• What happens if a Confounding variable is matched for?
• What kind of study can Overmatching be FATAL in?
A

OVERmatching

Matching on variables other than those that are risk factors for the disease under study

• Is a negative outcome resulting from what is normally a good practice:
• matching for potentially confounding variables*
• i.e., age-matched

EXAMPLE:

• If you use neighborhood controls in a study on NUTRITION and TUBERCULOSIS
• You are inadvertedly matching for SES (and thus, nutrition)*
121
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Observer Bias”

• Give an example using the Foot Study you’re in
A

OBSERVER BIAS:

Observers (or researchers) KNOW THE GOALS of the study or the hypotheses

• …and allow this knowledge to INFLUENCE their observations during the study

Foot Study Example:

• We want statistically significant data, so we “observe” things in a way that will help us get statistically significant data
122
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Demand Characteristics

• Give an example using the Foot Study you’re in
A

Demand Characteristics

• PARTICIPANTS form an interpretation of the experiment’s purpose
• …and unconsciously change their behavior*
• to FIT that interpretation*

Example:

• During doming trials, we remark how subject’s numbers were the “highest we’ve seen”
• This makes them think we WANT to see higher numbers
• They then strive to get the highest numbers possible

(In reality, we want NATURAL doming strengths)

123
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Information Bias”

• This definition is split up into 2 different definitions–
• depending on whether you’re dealing with Continuous or Categorical Variables
• What is it known by with each variable?
• Give an example using the Foot Study you’re in
• For both Continuous AND categorical variables
A

Information Bias

=Wrong or inexact recording of variables or data

• With CONTINUOUS Variables (such as blood pressure):
• this is referred to as Measurement Error”
• With CATEGORICAL Variables (such as tumor stage):
• this is known as “Misclassification”

​​Example:

• Continuous (measurements)
• recording them wrong
• Categorical (shoe size on form)
• you put down the wrong shoe size for them
124
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define a “Confounding Variable”

• Give a hypothetical example of a confounding variable (using Variable A,B, and C where C is the confounding variable)
• What is the “Placebo Effect?”
A

Confounding Variables (a.k.a. confounding factor, confounder)

• An extraneous variable that INFLUENCES the variables being studied
• ….but is NOT part of the _expected_ correlation or causal pathway being investigated

EXAMPLE:

• Suppose the relationship between A and B is being investigated
• A is hypothesized to increase B
• Experimental results support this hypothesis
• It is later discovered that a third variable (i.e., extraneous variable) influences both A and B,
• Decreasing A, and
• Increasing B
• In fact, it was C that created the observed result in B that was originally attributed to A

Variable C is a confounding variable

Placebo Effect:

• One example of a confounding variable
• The placebo effect occurs when participants given a placebo (i.e., sham treatment) during a study experience REAL or PERCEIVED health benefits–
• due to their belief that they ARE being treated
125
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define a “Detection Bias”

• Give an example
A

Detection Bias

• Systematic differences between groups…
• …caused by inconsistency in*
• the method of detection or diagnosis*

EXAMPLE:

• A study reports that inner city children suffer from ADHD at twice the rate of suburban children

It is later discovered that systematic differences existed between:

• diagnostic tools, and
• training available
• …at inner city hospitals included in the study
• …vs. suburban hospitals included in the study
• Basically, they’re detecting ADHD rates inconsistently, because suburban hospitals are able to more accurately diagnose those who REALLY have ADHD
• Inner city hospitals are quicker to just “slap a label” on ‘em
126
Q

Evaluating Research

Sources of Experimental Bias or Error

Define “Performance Bias”

• Give an example of this in a hospital scenario
A

Performance Bias

• Systematic differences between groups…
• in terms of the*

ACTUAL CARE OR TREATMENT PROVIDED

EXAMPLE:

• A physician unconsciously pays closer attention to and conducts more followup with patients the doctor knows to be enrolled in a heart study

This results in differences in care for those individuals not accounted for in the study

127
Q

Evaluating Research

• Sources of Experimental Bias or Error

Define “Experimenter Bias,” a.k.a., “_______ Bias”

• What are the 2 types of Experimenter Bias?

Apart from those 2 types, what else could be considered “Experimenter Bias?”

A

Experimenter Bias (a.k.a., Researcher Bias)

• ERRORS introduced into a study

due to the expectations of the investigator

• 2 Types:
1. Confirmation Bias
2. Reporting Bias

Experimenter Bias also can include unconscious communication of expected results to the participants

• …thereby influencing their behavior
128
Q

Evaluating Research

• Sources of Experimental Bias or Error

Experimenter Bias (2 types)

• Describe “Confirmation Bias”
A

Confirmation Bias:

• The tendency to favor information that confirms one’s hypothesis or preconceived notions
• and to dismiss information that discredits them

(This sort of thing also happens psychologically)

129
Q

Evaluating Research

• Sources of Experimental Bias or Error

Experimenter Bias (2 types)

• Describe “Reporting Bias”
A

Reporting Bias:

• Systematic differences resulting from some findings being reported and other findings NOT being reported
• Investigators may withhold or ignore data that does not support their hypothesis (if conscious and intentional this would be a clear violation of ethics)

Statistically-significant results are usually far more likely to be reported than are statistically insignificant results

…although BOTH are important to

an unbiased determination

130
Q

Research Design & Execution

• Measurement

Compare Accuracy vs. Precision

A

Accuracy

• is a measure of the degree to which a value represents the true or correct value

Precision

• is a measure of the degree to which repeatedly measured values show the same or reproducible values
• The greater the range or scatter of measurements, the less precision
131
Q

Research Design & Execution

• Measurement

Compare Reliability”withValidity”

• What results in LOW internal Validity?
A

RELIABILITY

• Results are consistent and repeatable

VALIDITY

• The test or experiment measures what it purports to measure
• …and uses methods that meet scientific standards

Failure to adhere to the standards of the scientific method or other accepted experimental best practices results in LOW internal validity

132
Q

Research Design & Execution

• Measurement

Reliability

• Compare “Test-Retest” reliability with “Inter-Rater” reliability

In both cases, a high value of reliability is around _._ and shows good _____ ______

A

“Test-Retest” reliability

• is a measure of the degree of consistency between one administration of a test and a subsequent administration of that same test (i.e. the “retest”)

“Inter-Rater” reliability

• is a measure of consistency between multiple raters or evaluators

that are assigning the same values or making

the same observations

In both cases, a high value of reliability is around 1.0 and shows good internal validity

133
Q

DECREASING“x” by a factor of 4 is the same as multiplying the variable (x) by?

A

1/4x

134
Q

Pv=nRT

• If the volume goes down by 80%, it has lost ___ of its value—
• which is the same as being multiplied by___
• or going down by a factor of _

A
• If the volume goes down by 80%, it has lost 4/5 of its value—
• which is the same as being multiplied by 1/5
• or going down by a factor of 5
135
Q

To say that something is 225% AS dense as something else is to say that it is ___% MORE dense

A

125%

Same as multiplying it by 5/4

136
Q

Correlation vs. Causation

• Linear Regression Analysis

Correlation Coefficient, r2

• Describe how the correlation coefficient relates to SCATTER PLOTS, aka Linear Regression Analysis (shown below)
• If the data points are CLOSER, as a whole, to the TREND LINE:
• Will you have a higher/lower r2 value?
A
• Correlation coefficients relate to linear regression analysis
• A large number of trials
• involving changes in the independent variable
• are graphed against the dependent variable

resulting in a SCATTER PLOT

• A “Least Squares” or “Best Fit” line is drawn
• that best approximates the trend of the data points

_The correlation coefficient, r2 is a measure of how tightly the DATA fit to THIS line!_

The CLOSER the data are, as a whole, to the trend line:

• the HIGHER the r2 value
137
Q

Correlation vs. Causation

• Linear Regression Analysis

Correlation Coefficient, r2

• Correlation coefficients vary from __ to __
• an r2 value of ___ would be a PERFECT correlation
• The correlation coefficient is said to explain the amount of variance in __ ACCOUNTED FOR by __
A

​Correlation Coefficients vary from 0 to 1

• An r2 value of 1.0 would be a PERFECT correlation!
• The correlation coefficient is said to explain the amount of variance in y ACCOUNTED FOR by x*
138
Q

Research Methods

• Describe HILL’S CRITERIA
• What are they used to EVALUATE in statistics?
• List the 9 Criteria
• ​Which is considered to be the MOST IMPORTANT of the 9?

HINT: The Crazy DRagon Caleb Poops The Silly String Always!

A

Hill’s Criteria is a SET OF GUIDELINES used to evaluate whether or not a CAUSAL RELATIONSHIP exists

1. Temporality
2. Consistency
3. Dose-Reponse Relationship
4. Coherence
5. Plausibility
6. Testable by Experiment
7. Specificity
8. Strength
9. Analogy
139
Q

Research Methods

HILL’S CRITERIA

• Describe “Temporality”
A

Temporality:

In time, the exposure or treatment

MUST PRECEDE THE OUTCOME

• If A causes B, A must occur in time prior to B

This is often considered the most important Hill Criteria

• If the outcome is found to occur before the exposure in even a few cases, the potential of causality is generally REJECTED
140
Q

Research Methods

HILL’S CRITERIA

• Describe “Strength”
• A larger ___ and _ -value, or a smaller _ -value

​​…. would all SUPPORT causation

A

Strength

• The MAGNITUDE of the CORRELATION or association
• including statistical measures of significance

A larger correlation, larger r2 value, or a smaller p value would all support causation

141
Q

Research Methods

HILL’S CRITERIA

• Describe “Consistency”
• “The correlative relationship is __________”
A

Consistency:

The correlation or association:

• continues across MULTIPLE trials, across time, or when the study is REPLICATED by others*
• In other words, the relationship is REPRODUCIBLE*
142
Q

Research Methods

HILL’S CRITERIA

• Describe “Specificity”
A

Specificity:

​One of the weakest Hill Criteria

• High specificity means the potential cause results in only ONE specific effect
• If present, it can provide additional support for causation–
• but the lack of specificity is common in MANY established cause-effect relationships
143
Q

Research Methods

HILL’S CRITERIA

• Describe “Plausibility”
A

Plausibility:

The association fits LOGICALLY within our current understanding of how a process works

The association has a logical theoretical basis

144
Q

Research Methods

HILL’S CRITERIA

• Describe “Dose-Response Relationship”
• The observed _____ is proportional TO?

Does meeting this criteria determine CAUSATION?

A

Dose-Response Relationship:

The observed response is proportional to:

the dosage or degree of the exposure or treatment

• This is considered STRONG evidence of causation*
• …but is NOT necessary*
145
Q

Research Methods

HILL’S CRITERIA

• Describe “Testable by Experiment”
• What IS it that needs to be “testable?”
A

Testable by Experiment:

The ASSOCIATION can be:

REPRODUCED VIA EXPERIMENT

146
Q

Research Methods

HILL’S CRITERIA

• Describe “Coherence”
• Association is compatible with…?
A

Coherence:

The ASSOCIATION is COMPATIBLE with:

existing or previously-established science

147
Q

Research Methods

HILL’S CRITERIA

• Describe “Analogy”
A

SIMILAR associations are shown

or known to exist that are:

analogous to the

association being considered

148
Q

Research Methods

• Define “INTERNAL Validity”
• “The extent to which you are able to say that…?”*
• What is internal validity a function of wrt the study itself?
A

Internal Validity

The extent to which you are able to say that:

No OTHER variables (except the one you’re studying) caused the result

…which is a function of the scientific RIGOR of the study

149
Q

Research Methods

• Differentiate b/t a CONDITION that is “Necessary” vs one that is merely “Sufficient
A

Necessary:

A condition that MUST be satisfied

in order for an event to occur

Sufficient:

A condition that, if satisfied,

GUARANTEES that an event WILL occur!

150
Q

Research Methods

• Define “EXTERNAL Validity”
• External Validity= ___________bility*
• What does external validity DEPEND heavily on?
• Give an example of a study with POOR external validity
A

External Validity = GENERALIZABILITY!

=The degree to which the findings can be

extrapolated to the general population

This depends a lot on:

the SUBJECTS being tested– are the representative of the general population?

Example:

• Sarah is a psychologist who teaches and does research at an expensive, private college.She’s interested in studying whether offering specific praise after a task will boost people’s self-esteem. If her hypothesis is correct, then giving someone a specific compliment on a job well done after a task will make them feel better about themselves. And if she can show that specific praise post-task boosts self-esteem, then managers at companies everywhere will be able to boost their employees’ self-esteem by offering them specific praise.*
• But here’s a problem: the volunteers that Sarah gets for her study are all college students, most of them are white, and most of them are from privileged backgrounds*

Sarah worries that her results might not be applicable to people who are NOT in their late teens or early 20s, white, and rich

151
Q

Research Methods

• Define a SINGLE-BLIND test
• What all information gets witheld (3), and from whom?
A

• The study itself
• Who is in the…
• Control groups
• Treatment groups
• other potentially biasing details

are concealed from:

the person DOING the assessment

152
Q

Research Methods

• Define a DOUBLE-BLIND test
• What all information gets witheld (3), and from whom?
A

• The study itself
• Who is in the…
• Control groups
• Treatment groups
• other potentially biasing details

are concealed from BOTH:

1. The SUBJECT
2. The person DOING the assessment (researcher)​

*

153
Q

Research Methods

• Type I vs Type II Errors
A

Type I Error: REJECTED H0** but **SHOULDN’T HAVE

CLAIMED difference between groups

…when NONE existed

Type II ErrorSHOULD** **HAVE** rejected H0 but **DIDN’T

Did NOT claim a difference between groups

…when one DID exist

154
Q

Research Methods

• Define the NULL HYPOTHESIS, H0
• What is the Null hypothesis when testing for:
1. ​​Group differences
2. Correlation or Causation
A

The Null Hypothesis:

H0 is always the LACK OF A RELATIONSHIP

OR GROUP DIFFERENCE

In testing for GROUP DIFFERENCES:

• H<strong>0</strong> = there are NO statistically significant differences between groups

When testing for CORRELATION or CAUSATION:

H0= there is NO RELATIONSHIP

155
Q

Research Methods

• Define the ALTERNATIVE HYPOTHESIS
• What is the Alt. Hypothesis when testing for:
1. ​​Group differences
2. Correlation or Causation
A

The Alternative Hypothesis:

The PRESENCE OF A RELATIONSHIP OR GROUP DIFFERENCE

The OPPOSITE of the H0!

In testing for group differences:

• Alt. Hypothesis=there IS a difference between groups

When testing for correlation or causation:

• Alt. Hypothesis= there IS a relationship.
156
Q

Statistics

• Compare & Contrast “Sample” vs. “Population”
A

Sample =

• that portion of a population included in the data
• [e.g., U.S. veterans called in the phone survey]

SMALLER than the POPULATION

which is ALL the members in the group/category being sampled

[e.g., ALL U.S. veterans]

157
Q

Statistics

• Define a STATISTIC
A
• A statistic is a measure or data point*
• (such as mean, median, etc.)*
• that is calculated for the sample*

[e.g., average income among U.S. veterans in a sample]

158
Q

Statistics

• Define “Parameter
• How does it compare with a Statistic?
A

Parameter =

a measure (such as mean, median, etc.) that is calculated for the ENTIRE population,

NOT merely the sample of the population

[e.g., average income among ALL U.S. veterans]

159
Q

Statistics

• Compare Mean, Median, & Mode
• Also, define Range
A

MEAN=

• the average value

MEDIAN =

• the middle of the data

MODE=

• the most frequently occurring data point

RANGE=

• the minimum data point ⇒maximum data point
160
Q

Statistics

• Define STANDARD DEVIATION (SD)
• What does a SMALL SD indicate?
• What does a LARGE SD indicate?
A

STANDARD DEVIATION (SD) =

• how tightly associated the data are TO THE MEAN

A small SD indicates:

a narrow set of data for which most values are close to the mean

A large SD indicates:

a greater spread, or wider distribution,

of the data around the mean

161
Q

Standard Deviation (SD)

• Define “Normal Distribution
• 1 SD=__% of the population
• 2 SD=__% of the population
• 3 SD=__% of the population
A

Normal Distribution=

Hypothetical perfect BELL-SHAPED curve f_or which the following is true:_

• 1 SD = 68% of the population
• 2 SD = 95% of the population
• 3 SD = 99% of the population
162
Q

Research Design

When it comes to Probability:

• The ASSUMPTION is that the OUTCOMES are _____ and _____ ________
A

Assumption = Outcomes are:

1. INDEPENDENT
• Do NOT influence one another
2. MUTUALLY EXCLUSIVE
• They CANNOT occur together
163
Q

Probability

• “And” vs. “Or”
• To solve for each:
• “______ the probabilities of individual events to get the overall probability of ____ events occurring”
A

AND vs. OR

AND =

• MULTIPLY the probabilities of individual events to get the overall probability of BOTH events occurring

OR =

• ADD the probabilities of each individual event together to get the overall probability of EITHER event occurring
164
Q

Hypothesis Testing

• t-test or z-test
• What is the t-test or z-test (once calculated) COMPARED to?
• What does this comparison give us?
A

t-Test or z-Test ⇒p value

The “test statistic” (either a t-value or a z-value) is calculated

• This result is COMPARED to:
• a TABLE of t-values or z-values

The table gives us:

the SIGNIFICANCE LEVEL, “α,” associated with that test statistic

165
Q

Hypothesis Testing

• t-test or z-test
• For a z-test,n” (sample size) must be GREATER THAN what number?
• Once this criteria is met, what can we assume about the distribution?
• When would we have to use a t-test?
• ​What does THIS mean wrt its distribution?
A

z-test

• For a z-test, n>30
• ​This will mean it has a NORMAL (BELL-SHAPED) DISTRIBUTION
• We’d use a t-test when our sample size is SMALL
• ​This will mean our graph will NOT be normally distributed (only approximately)
166
Q

Hypothesis Testing

Significance Level, “α”

• What are the 3 α values we’ll see?

When comparing p and α values:

• what does a p value > .005 indicate wrt level of confidence?
A

Significance Level (α)

• α = 0.05, 0.01, 0.001*
• p < 0.05 means we can be 95% confident that the results are actual/real
• RATHER than the result of random chance*
• p < 0.01 means we can be 99% confident, and so forth