WGU C207 guide
1. Missing data and refusals: This misuse occurs when a certain part of thesample gets lost
or subjects refuse to contribute ti the overall data collection
2. Small sample size: Sample size is too small to make inferences
3. Parametric test: Assumes there is a structure (maybe a normal distribution) tothe
population, often appearing when mean or standard deviation are important
4. Non-parametric test: Does not assume there to be a structure (maybe a normaldistribution)
to the population
5. Association and causality misuse: When a researcher notices a relationshipbetween two
variable and assumes that one variable is the cause of the other. In reality, these variables
might both be caused by separate variables; they would merely be correlated. Or have no
relationship at all.
6. Training and test data misuse: When the same dat that's used to forma hypothesis is then
used to test the hypothesis. Sample size is too small and differentsamples have a lot of crossover
7. Unfounded assumptions: Assumption is made that has not been proven
8. Operationalization: The development of specific research procedures that al-low for
observation and measurement of abstract concepts. Key aspects of op- erationalizing is
defining variables and attributes that adequately represent the concepts of the study.
9. Blinding: When researchers place barriers between themselves and subjectsto insure the
researchers do not influence subjects' behavior.
10. Causation: Cause and effect
11. Confidence Interval: The range around the mean that a sample has a specificprobability of
occurring within
12. Probability: The likelihood of an event occurring
13. Compliment: The occurrence of an event not happening, the opposite. The sumof the
probability and its compliment is always equal to one.
14. Independent Events: Events that are not effected by other trials or events,
15. Complimentary events: Events with two outcomes that are the only possibleoutcome.
Like flipping a coin.
16. Conditional Probability: The probability of an event occurring, given that an-other event
has already occurred.
P(B|A) = P(A and B)/P(A)
17. Dependent Events: An event that is affected by the previous events.
18. Probability of an Intersection: The probability of two independent events hap-pening.
P(A)B) = P(A) x P(B)
,19. Probability of a Union: The collection of elements that are both A, B or both Aand B.
P (AUB) = P (A) + P (B) - P (A)B)
20. Mutually Exclusive Events: When two or more events are not able to occur atthe same
time
21. Multiplication Principle: When the probabilities of multiple events are multi-plied
together to determine the likelihood of all the event occurring.
22. Inferential Statistics: Statistics that are used to make predictions about apopulation from
observations of a sample
23. Descriptive Statistics: Used to describe a population from observations of thatwhole
population
24. Sampling with Replacement: A technique used when each piece of the popu-lation can be
selected more than once.
If sampling with replacement and taking a sample of size n from a population of z,there are
z^n possible outcomes.
25. Sampling without Replacement: A technique used when each piece of thepopulation can
only be selected once.
If you take a sample size of n without replacement, there are usually n! (n factorial)possible
outcomes. i.e. n! = 6! = 6 x 5 x 4 x 3 x 2 x 1 = 720
26. Random Errors: Error in measurement caused by unpredictable statistical fluc-tuations
27. Information Bias: A prejudice in the data that results when either the respondentor the
interviewer has an agenda and is not presenting impartial questions or responding with truly
honest responses, respectively
28. Ratio Data: Similar to interval data in that the data is ordered within a range andwith
each data point being an equal interval apart, also has a natural zero point which indicates
none of the given quality
29. Data Set: A collection of related data records on a storage device.
30. Nominal Data: Sometimes called categorical data or qualitative data, this datatype is
used to label subjects or data by name
31. Reliable Data: Data that is consistent and repeatable
32. Davenport-Kim Three Stages Model: A decision-making model developed byThomas
Davenport and Jinho Kim that consists of three stages:
Framing the problem Solving the problem Communicating the problem
33. Blind study: A study performed where the participants are not told if they are inthe
treatment group or control group
34. Double-Blind Study: A study performed where neither the treatment allocatorsnor the
, participant knows which group the participant is in
35. Measurement Bias: A prejudice in the data that results when the sample is not
representative of the population being represented.
36. Analytics: The discovery, analysis, and communication of meaningful patternsin data.
37. Data Management: The management, including the cleaning and storage, ofcollected data
38. Triple-Blind Study: A study performed where neither the treatment allocator northe
participant nor the response gatherer knows which group the participant is in
39. Omission Error: An error because something ( for example, data or surveyresponses) is
missing.
40. Relational Database: A database structured to recognize relations amongstored items of
information
41. Ordinal Data: Data that places data objects into an order according o somequality with
higher order indicating more of that quality
42. Discrete Data: Data that can only take on whole values and has clear bound-aries
43. Interval Data: Data that is ordered within a range and with each data point beingan equal
interval apart
44. Valid Data: Data resulting from a test that accurately measures what it isintended to
measure
45. Big Data: A catch-phrase that describes a massive volume that is so large thatit's difficult
to process using traditional database software techniques
46. Systematic errors: Errors in measurement that are constant within a data set,sometimes
caused by faulty equipment or bias.
47. Benchmarks: Standards or points of reference for an industry or sector that canbe used for
comparison and evaluation.
48. Continuous Data: Data that can lay along any point in a range of data
49. Statistics: The science that deals with the interpretation of numerical facts ordata though
theories of probability. Also, the numerical facts or data themselves.
50. Decision Tree Analysis: The diagram of possible alternatives and their expect- ed
consequences in order to formulate passible courses of action in order to makedecisions
51. Expected Monetary Value (EMV) Analysis: A statistical technique that calcu- lates the
average outcome when the future includes scenarios that may or may nothappen.
52. Response Rates: The number of people who respond to a marketing offer (oftenexpressed as
a percentage of the number of responses divided by the number of people that ere targeted
mail, email etc.)
1. Missing data and refusals: This misuse occurs when a certain part of thesample gets lost
or subjects refuse to contribute ti the overall data collection
2. Small sample size: Sample size is too small to make inferences
3. Parametric test: Assumes there is a structure (maybe a normal distribution) tothe
population, often appearing when mean or standard deviation are important
4. Non-parametric test: Does not assume there to be a structure (maybe a normaldistribution)
to the population
5. Association and causality misuse: When a researcher notices a relationshipbetween two
variable and assumes that one variable is the cause of the other. In reality, these variables
might both be caused by separate variables; they would merely be correlated. Or have no
relationship at all.
6. Training and test data misuse: When the same dat that's used to forma hypothesis is then
used to test the hypothesis. Sample size is too small and differentsamples have a lot of crossover
7. Unfounded assumptions: Assumption is made that has not been proven
8. Operationalization: The development of specific research procedures that al-low for
observation and measurement of abstract concepts. Key aspects of op- erationalizing is
defining variables and attributes that adequately represent the concepts of the study.
9. Blinding: When researchers place barriers between themselves and subjectsto insure the
researchers do not influence subjects' behavior.
10. Causation: Cause and effect
11. Confidence Interval: The range around the mean that a sample has a specificprobability of
occurring within
12. Probability: The likelihood of an event occurring
13. Compliment: The occurrence of an event not happening, the opposite. The sumof the
probability and its compliment is always equal to one.
14. Independent Events: Events that are not effected by other trials or events,
15. Complimentary events: Events with two outcomes that are the only possibleoutcome.
Like flipping a coin.
16. Conditional Probability: The probability of an event occurring, given that an-other event
has already occurred.
P(B|A) = P(A and B)/P(A)
17. Dependent Events: An event that is affected by the previous events.
18. Probability of an Intersection: The probability of two independent events hap-pening.
P(A)B) = P(A) x P(B)
,19. Probability of a Union: The collection of elements that are both A, B or both Aand B.
P (AUB) = P (A) + P (B) - P (A)B)
20. Mutually Exclusive Events: When two or more events are not able to occur atthe same
time
21. Multiplication Principle: When the probabilities of multiple events are multi-plied
together to determine the likelihood of all the event occurring.
22. Inferential Statistics: Statistics that are used to make predictions about apopulation from
observations of a sample
23. Descriptive Statistics: Used to describe a population from observations of thatwhole
population
24. Sampling with Replacement: A technique used when each piece of the popu-lation can be
selected more than once.
If sampling with replacement and taking a sample of size n from a population of z,there are
z^n possible outcomes.
25. Sampling without Replacement: A technique used when each piece of thepopulation can
only be selected once.
If you take a sample size of n without replacement, there are usually n! (n factorial)possible
outcomes. i.e. n! = 6! = 6 x 5 x 4 x 3 x 2 x 1 = 720
26. Random Errors: Error in measurement caused by unpredictable statistical fluc-tuations
27. Information Bias: A prejudice in the data that results when either the respondentor the
interviewer has an agenda and is not presenting impartial questions or responding with truly
honest responses, respectively
28. Ratio Data: Similar to interval data in that the data is ordered within a range andwith
each data point being an equal interval apart, also has a natural zero point which indicates
none of the given quality
29. Data Set: A collection of related data records on a storage device.
30. Nominal Data: Sometimes called categorical data or qualitative data, this datatype is
used to label subjects or data by name
31. Reliable Data: Data that is consistent and repeatable
32. Davenport-Kim Three Stages Model: A decision-making model developed byThomas
Davenport and Jinho Kim that consists of three stages:
Framing the problem Solving the problem Communicating the problem
33. Blind study: A study performed where the participants are not told if they are inthe
treatment group or control group
34. Double-Blind Study: A study performed where neither the treatment allocatorsnor the
, participant knows which group the participant is in
35. Measurement Bias: A prejudice in the data that results when the sample is not
representative of the population being represented.
36. Analytics: The discovery, analysis, and communication of meaningful patternsin data.
37. Data Management: The management, including the cleaning and storage, ofcollected data
38. Triple-Blind Study: A study performed where neither the treatment allocator northe
participant nor the response gatherer knows which group the participant is in
39. Omission Error: An error because something ( for example, data or surveyresponses) is
missing.
40. Relational Database: A database structured to recognize relations amongstored items of
information
41. Ordinal Data: Data that places data objects into an order according o somequality with
higher order indicating more of that quality
42. Discrete Data: Data that can only take on whole values and has clear bound-aries
43. Interval Data: Data that is ordered within a range and with each data point beingan equal
interval apart
44. Valid Data: Data resulting from a test that accurately measures what it isintended to
measure
45. Big Data: A catch-phrase that describes a massive volume that is so large thatit's difficult
to process using traditional database software techniques
46. Systematic errors: Errors in measurement that are constant within a data set,sometimes
caused by faulty equipment or bias.
47. Benchmarks: Standards or points of reference for an industry or sector that canbe used for
comparison and evaluation.
48. Continuous Data: Data that can lay along any point in a range of data
49. Statistics: The science that deals with the interpretation of numerical facts ordata though
theories of probability. Also, the numerical facts or data themselves.
50. Decision Tree Analysis: The diagram of possible alternatives and their expect- ed
consequences in order to formulate passible courses of action in order to makedecisions
51. Expected Monetary Value (EMV) Analysis: A statistical technique that calcu- lates the
average outcome when the future includes scenarios that may or may nothappen.
52. Response Rates: The number of people who respond to a marketing offer (oftenexpressed as
a percentage of the number of responses divided by the number of people that ere targeted
mail, email etc.)
