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BIOSTAT > INTRO > Flashcards

INTRO Flashcards

1
Q

is the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.

A

Statistics

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2
Q

a valuable tool in making sense of data in the information age

A

Statistics

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3
Q

is the development and application of statistical concepts and techniques to biological sciences

A

Biostatistics

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4
Q

subcategory of statistics, it is statistics applied to biology

A

Biostatistics

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5
Q

variable in a particular table of data are column-headers
a characteristic or attribute that can assume different values
e.g. age, sex,

A

Variable

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6
Q

a variable that can have values that are determined by chance
yet to be determined, may still assume different values
e.g. age - ages of participants are yet to be determined

A

Random Variable

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7
Q

variable with data that is already known because it is already pre-recorded
it is already determined
e.g. date - a non-random variable because date cannot assume different values, date is already determined by convention

A

Non-random Variable

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8
Q

values that the variables can assume
below the headers in a table of data
can be determined through measurement or observation

A

Data

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9
Q

collection of data values
table of data

A

Data

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10
Q

each value in a data set
individual values

A

Data value or datum

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11
Q

consists of all the subjects that fits the criteria

A

Population

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12
Q

group of subjects selected from the population

A

Sample

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13
Q

is a decision making process for evaluating claims about a population

A

Hypothesis testing

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14
Q

collection, organization, summarization, and presentation of data
describing a situation
merely describing the data

A

Descriptive Statistics

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15
Q

e.g. census (income, family members) taken from the whole population, survey is the same as census but it only takes a sample from a given population

A

Descriptive Statistics

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16
Q

EXAMPLE:
Male - 51%
Female - 49%

A

Descriptive Statistics

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17
Q

generalizing from samples to populations; concept of probability (chance of an event occuring) is used

e.g. When studying the average grade of MLS-1 students in BE-100 with the population of 3725. A sample of 100 students from the population is taken and the average of these students is determined. For as long as the 100 samples are chosen using probabilistic methods, the conclusion taken from this sample is probably true for the whole population.

A

Inferential Statistics

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18
Q

performing estimations and hypothesis tests;

e.g. testing the claim that the average age of MLS students is 23

A

Inferential Statistics

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19
Q

determining relationships among variables; and

e.g. relationship between the number of hours studying and the final grade

A
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20
Q

making predictions

e.g. if there is a relationship between variables, predictions can be made

describing and drawing conclusions from a given data

A

Inferential Statistics

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21
Q

variables that can be placed into distinct categories, according to some characteristic or attribute
numbers can be used but for labeling only (e.g. 0 for male, 1 for female in an excel sheet)

e.g. sex (male/female), gender, program

A

Qualitative Variable

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22
Q

are numerical and can be ordered or ranked

e.g. age, grades, blood glucose level

A

Quantitative Variable

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23
Q

Quantitative Variable: 2 types

A

discrete variables
continuous variables

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24
Q

assume values that can be counted
e.g. number of participants, number of siblings (counting numbers)
e.g. 1,2,3

A

discrete variables

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25
Q

can assume an infinite number of values between any two specific values
e.g. arm length/span
e.g. 1.1, 1.2, 1.3

A

continuous variables

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26
Q

can assume an infinite number of values between any two specific values
e.g. arm length/span
e.g. 1.1, 1.2, 1.3

A

continuous variables

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27
Q

______________ must be measured, answers must be rounded off because of the limits of the measuring device.

A

continuous data

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28
Q

Measurement Scales

A

Nominal Level of Measurement
Ordinal Level of Measurement
Interval Level of Measurement
Ratio Level of Measurement

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29
Q

classifies data into mutually exclusive (nonoverlapping), exhausting categories in which no order or ranking can be imposed on the data
categorical in nature

A

Nominal Level of Measurement

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30
Q

e.g. religion (Christian, Jewish, Islam, and others) - Christian cannot be Jewish, Jewish cannot be Islam, etc. as it is a nonoverlapping data.

A

Nominal Level of Measurement

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31
Q

e.g. zip codes (no meaningful order or ranking, just for labeling cities)

A
32
Q

it is simply for naming or categorical purposes

A

Nominal Level of Measurement

33
Q

classifies data into categories that can be ranked; however, precise differences between the ranks do not exist

A

Ordinal Level of Measurement

34
Q

higher than nominal, has all the characteristics of nominal and can now be ranked

A

Ordinal Level of Measurement

35
Q

e.g. 1st yr, 2nd yr, 3rd yr - categorical as it is used to label a student, and can also be ranked in the sense that students with the higher level can be recognized (3rd yr being the highest)

A

Ordinal Level of Measurement

36
Q

precise differences between ranks do not exist

e.g. superior, average, poor in a student evaluation - differences between the superior and the average professor, may not be the same as the difference between the average and the poor.

Gaps are not precisely defined.

A

Ordinal Level of Measurement

37
Q

ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero

A

Interval Level of Measurement

38
Q

a higher level of measurement than the ordinal because the data can be ranked, and the gaps between units exists

A

Interval Level of Measurement

39
Q

e.g. Biostat final grades of 90, 80, and 70 - the grades can be ranked from highest to lowest difference of 10 between 90 and 80, and 80 and 70
the difference between interval and ratio level is that interval has no meaningful zero

A

Interval Level of Measurement

40
Q

e.g lowest grade cannot be 0

A

Interval Level of Measurement

41
Q

e.g. temperature, 0°C is still a temperature, in the sense that it is still meaningful, there are even temperatures below zero

A

Interval Level of Measurement

42
Q

possesses all the characteristics of interval measurement, and there exists a true zero

A

Ratio Level of Measurement

43
Q

true ratios exist when the same variable is measured on two different members of the population

A

Ratio Level of Measurement

44
Q

e.g. height of 152 cm, 132 cm, 121 cm - these can be ranked from tallest to shortest, intervals are the same between values (rational), there exists a true zero (a height of 0 cm is not possible)

A

Ratio Level of Measurement

45
Q

Temperature in celsius or fahrenheit falls under ________

A

interval-level

46
Q

Kelvin falls under _________ as kelvin starts at 0.

A

ratio-level

47
Q

Interval and Ratio falls under ____________

A

Numerical or Quantitative Data

48
Q

3 types of data collection

A

Survey
Surveying records
Direct observations

49
Q

PROBABILISTIC SAMPLING

A

Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling

50
Q

samples are selected by using chance methods or random numbers

simplest method of sampling

disadvantage of random sampling is it is tedious

Ensures that samples are unbiasedly chosen

e.g. fishbowl method

A

Random Sampling

51
Q

numbering each subject of the population and then selecting every kth subject

still a random type of sampling but differs in the methods used

samples should reflective of the population

A

Systematic Sampling

52
Q

dividing the population into groups (called strata) according to some characteristic that is important to the study, then sampling from each group

samples within the strata should be randomly selected

e.g. if the entire population is 40% male and 60 % female, the sample should reflect the same. For instance, if 10 participants are chosen as the sample, it should consist of 4 males and 6 females.

A

Stratified Sampling

53
Q

population is divided into groups called clusters by some means such as geographic area

then the researcher randomly selects some of these clusters and uses all members of the selected clusters as the subjects of the samples

A

Cluster Sampling

54
Q

NON PROBABILISTIC SAMPLING

A

Convenience Sampling
Voluntary response Sampling
Snowball Sampling

55
Q

is a non-probability sampling method where units are selected for inclusion in the sample due to convenience

A

Convenience Sampling

56
Q

a voluntary response sample can be defined as a sample made up of participants who have voluntarily chosen to participate as a part of the sample group

A

Voluntary response Sampling

57
Q

is a recruitment technique in which research participants are asked to assist researchers in identifying other potential subjects

A

Snowball Sampling

58
Q

Statistical Studies: 2 types

A

Observational Study
Experimental Study

59
Q

the researcher merely observes what is happening or what has happened in

the past and tries to draw conclusions based on these observations
no interventions or manipulations, variables cannot be controlled

A

Observational Study

60
Q

advantage is it occurs in a natural setting, and it can be done in situations considered unethical or dangerous to conduct an experiment

A
61
Q

advantage is it occurs in a natural setting, and it can be done in situations considered unethical or dangerous to conduct an experiment

disadvantages are; the variables are not controlled, and a cause-effect relationship cannot be defined clearly depending on how the study is designed, can be expensive and time-consuming, and the researcher may not be using his/her own measurement so results can be inaccurate

A

Observational Study

62
Q

the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables

e.g. clinical trials - manipulating the participants, if they belong to the control or treatment group

A

Experimental Study

63
Q

cause-and-effect is clear as the variables are controlled by researchers

A

Experimental Study

64
Q

disadvantages are; it occurs in an unnatural setting as it is controlled, the study should pass high ethical standards, and the Hawthorne Effect (when subjects of an experimental study attempt to change or improve their behavior simply because it is being evaluated or studied)

in order to avoid the Hawthorne Effect, clinical trials are now blind studies

A

Experimental Study

65
Q

Variables in Statistical Studies: 3

A
  1. Independent Variable (Explanatory Variable)
  2. Dependent Variable (Outcome Variable)
  3. Confounding Variable
66
Q

the variable being manipulated by the researcher

A

Independent Variable (Explanatory Variable)

67
Q

dependent on the independent variable

resultant variable

heavily affected by the independent variable

A

Dependent Variable (Outcome Variable)

68
Q

a variable that influences the dependent, but not separated from the independent variable

those that affect other variables in a way that produces spurious or distorted associations between two variables

A

Confounding Variable

69
Q

if the sample size is too small, it might not be conclusive
if the samples are selected purposefully or biasedly, probabilistic sampling methods are not used
falsely skew results

A

Suspect Samples

70
Q

averages can be deceptive
difference between mean (heavily influenced by outliers) and median (not heavily influenced by outliers)
choosing between mean and median depends upon the data set, it’s better to choose median when the data set consists of outliers, and mean if it’s the opposite

A

Ambiguous Averages

71
Q

the use of absolute (number) and the relative (percentage) number

A

Changing The Subject

72
Q

one in which no comparison is made
statistics must be in context

A

Detached Statistics

73
Q

relationships between ideas are any that are not specifically stated in the passage

A

Implied Connections

74
Q

no sense of numbers or labels in the axis

A

Misleading Graphs

75
Q

the way questions are asked, leading questions
Recall Bias - a type of bias that occurs when participants in a research study or clinical trial do not accurately remember a past event or experience or leave out details when reporting about them

A

Faulty Survey Questions

BIOSTAT (25 decks)

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