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Mathematics > Data Management > Flashcards

Data Management Flashcards

1
Q

Latin word meaning “the state”

A

status

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2
Q

Came from a Latin word “status”

A

Statistics

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3
Q

branch of science that deals with the collection, presentation, organization, analysis and interpretation of data
study of variation

A

Statistics

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4
Q

Empowers us to make intelligent choices

A

Information

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5
Q

allows us to answer problems by giving a clear picture of a particular collection of elements

A

Statistical Inquiry

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6
Q

Collection of all elements under consideration

A

Population

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7
Q

Subset of a population from which raw data are being obtained

A

Sample

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8
Q

The specification of interests depends on this.

A

Scope of the study

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9
Q

Characteristics or attributes of the elements in a collection

A

Variables

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10
Q

realized value of a variable

A

Observation

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11
Q

collection of observations

A

Data

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12
Q

Data set consist of some basic measurements of individual items

A

Data Structure

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13
Q

3 basic ways of classifying data set

A
  1. number of variables
  2. kind of information
  3. time sequence/cross sectional
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14
Q

summary measure describing the specific characteristic of the population

A

Parameter

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15
Q

summary measure describing the specific characteristics of the sample

A

Statistics

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16
Q

organizing and summarizing data
deals with the techniques used in the collection, presentation, organization and analysis of the data on hand
used to say something or describe a set of information collected
represented with graphs

A

Descriptive Statistics

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17
Q

come up with generalizations or inferences about the population using the information in the selected sample
used to say something about a larger group using information collected from a small part of that group

A

Inferential Statistics

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18
Q

It is based on good procedures for producing data and thoughtful examination of data.

A

Effective interpretation of data

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19
Q

fancy way of saying we are estimating population values based on your sample data

A

Estimation Statistics

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20
Q

drawing conclusion about a population parameter
uses data to decide between two or more different possibilities
produces a definite decision

A

Hypothesis Testing

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21
Q

designed research to provide information needed to solve a research problem

A

Statistical Inquiry

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22
Q

most important step in statistical study

A

Determining the Sample Size

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23
Q

tells the researcher how sure the responses of the sample represent the population

A

Confidence Level

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24
Q

how much a percentage points deviate from the real population value

A

Margin of Error

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25
Q

applicable only when estimating a population proportion and when the confidence level is 95%

A

Slovin’s Formula

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26
Q

Process of selecting a representative group from the population under study

A

Sampling

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27
Q

list of all items in your population
complete list of everyone/everything you want to study
specific

A

Sampling Frame

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28
Q

total group of individuals from which the sample might be drawn

A

Target Population

29
Q

group of people who take part in an investigation, called “participants”

A

Sample

30
Q

extent to which we can apply the findings of our research to the target population

A

Generalizability

31
Q

Non Probabilistic Sampling Techniques

A

Convenience Sampling - subjective basis of selection
Purposive Sampling - used in exploratory research
Referral/Snowball Sampling - produces biased results
Quota Sampling - hypothesis was generated

32
Q

Probabilistic Sampling Techniques

A

Simple Random Sampling - random basis of selection
Systematic Random Sampling - used in conclusive research
Stratified Random Sampling - can make statistical inferences
Cluster Sampling - hypothesis was tested

33
Q

based on personal choice
does not follow a randomization mechanism
allow researcher to choose the elements in the sample subjectively

A

Non-Probabilistic Sampling

34
Q

uses random selection wherein each element in the sampled population has equal chances of being selected

A

Probabilistic Sampling

35
Q

Allows us to calculate an ideal sample size and appropriate especially in situations with larger population

A

Cochran’s Sample Size Formula

36
Q

4 things to consider when using Cochran’s Formula

A
  1. Population
  2. Level of Precision (Margin of Error)
  3. Confidence Level/ Risk Level
  4. Standard Deviation/ Degree of Variability
37
Q

the more sample you examine, the better the results will be

A

Law of Large

38
Q

values are determined by chance

A

Variables

39
Q

placed into distinct categories, categorical

A

Qualitative Variables

40
Q

order or ranked, numerical

A

Quantitative Variables

41
Q

used to give overview of data via rows and columns
enables the reader to look up specific information
used to display individual values and compare to other
used to present more precise values of data

A

Tables

42
Q

used to represent data by using vertical/horizontal bars
used when the data we want to present are qualitative

A

Bar Graph

43
Q

used to represent data that occurs over a specific period
used to see patterns on the increase/decrease of values over time

A

Line Graph

44
Q

used to represent data that are in percentage or proportion
used to describe composition of data or how one part contributes to the whole

A

Pie Graph

45
Q

most commonly used measure of central tendency
what most people think of as average
most appropriate when data are in interval or ratio scale
only one value of the mean for the given set of values
easily influenced by extreme values

A

Mean

46
Q

extremely high or low value compared to other values

A

Outlier

47
Q

value in the data set which occurs most frequently
used for nominal data
have one or more modes
least reliable measure of center
quick approximation of average

A

Mode

48
Q

middle value of a given set of measurements
not influenced by extreme values

A

Median

49
Q

arrangement of values in an increasing or decreasing order

A

Array

50
Q

Which average should be used in numerical data?

A

Mean/Median

51
Q

Which average should be used in categorical data?

A

Mode/Median

52
Q

not only specifying the measure of central tendency but also the measure of dispersion

A

Measure of Variation

53
Q

measure of variation that is most appropriate to any numerical data

A

Standard Deviation

54
Q

most appropriately used when you have two data sets with different unit of measurement you want to compare

A

Coefficient of Variation

55
Q

Measures of variation are essentially —- for categorical data

A

Non-existent

56
Q

if categorical data, it is most appropriate to describe the variation by —

A

Identifying extreme scores

57
Q

used when the researcher wants to generalize about a population given a sample
based on hypothesis

A

Statistical Tests

58
Q

conjecture about the population parameter that may or may not be true
Null/Alternative Hypothesis

A

Statistical Hypothesis

59
Q

numeric characteristics computed from the sample from which the decision to reject or fail to reject the null hypothesis is based

A

Test Statistic

60
Q

size of a risk of erroneously rejection the null that the researcher is willing to make

A

Level of Significance

61
Q

assumptions of the statistical test are met
sample size is large
data are numerical

A

Parametric

62
Q

at least one assumption of the statistical test is not met
sample size is too small
data are categorical

A

Non-Parametric

63
Q

multiple comparison of the mean

A

ANOVA and Kruskall-Wallis Tests

64
Q

pairwise comparison of the mean

A

Tukey’s HSD and Dunn’s Test

65
Q

There is enough evidence to reject the claim

A

Reject Null Hypothesis

66
Q

There is not enough evidence to reject the claim

A

Fail to Reject Null Hypothesis

67
Q

There is enough evidence to support the claim

A

Reject Alternative Hypothesis

68
Q

There is not enough evidence to support the claim

A

Fail to Reject Alternative Hypothesis

Mathematics (1 decks)

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