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SECOND YR > BIOSTATS [PRELIMS] > Flashcards

BIOSTATS [PRELIMS] Flashcards

1
Q

a set of data or a mass of observations

A

STATISTICS

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2
Q

collection, organization, analysis and interpretation of numerical data

A

STATISTICS

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2
Q

Application of statistics to biological problems

A

BIOSTATISTICS

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2
Q

vital statistics, health statistics

A

STATISTICS

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3
Q

tendency of a measurable
characteristics to change with respect to person, place & time e.g. weight, age, height, etc.

A

VARIATION

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4
Q

statistical techniques for summarizing

A

DESCRIPTIVE STATISTICS

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5
Q

Present data in a form that will make them easier to ANALYZE and INTERPRET

A

Descriptive Data

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6
Q

counts, proportions, tables, graphs, etc.

A

DESCRIPTIVE DATA

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7
Q

estimates, predictions, generalizations and conclusions about a target population

A

INFERENTIAL STATISTICS

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8
Q

TWO TYPES OF INFERENTIAL (key components in distinguishing the branch of inferential)

A

a. estimation
b. hypothesis testing

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9
Q

it measures and count statistics

A

data

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10
Q

raw material of statistics

A

data

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11
Q

(hindi nababago) fix or unchanging measurements / phenomenon

A

CONSTANT

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12
Q

(nababago and inconsistent) changing or inconsistent measurements cannot be predicted with certainty

A

VARIABLE

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13
Q

Title: “The Effect of Sleep Duration on Academic Performance among High School Students”

Is “Sleep Duration” a constant or a variable?

A

VARIABLE

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14
Q

Title: “The Relationship Between Income Level and Healthcare Access in Urban Areas”

Is “Income Level” a constant or a variable?

A

VARIABLE

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15
Q

In a study measuring blood pressure across different age groups, the blood pressure cuff is set to 120 mmHg for all participants during testing.

Is “120 mmHg” a constant or a variable?

A

CONSTANT

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16
Q

In a study, all participants are given the same questionnaire with identical questions.

Is the “Questionnaire” a constant or a variable?

A

CONSTANT

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16
Q

Title: “The Impact of Exercise Frequency on Mental Health in College Students”

Is “College Students” a constant or a variable?

A

CONSTANT

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17
Q

In an experiment on plant growth, each plant is given the same amount of water daily (500 ml).

Is “500 ml of water” a constant or a variable?

A

CONSTANT

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18
Q

Title: “The Effect of Study Habits on Grades among University Freshmen”

Is “Grades” a constant or a variable?

A

VARIABLE

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19
Q

Title: “The Relationship Between Social Media Use and Self-Esteem in Adolescents”

Is “Self-Esteem” a constant or a variable?

A

VARIABLE

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20
Q

In a survey, participants are all aged 30 years old.

Is “Age” a constant or a variable?

A

CONSTANT

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21
Q

Title: “The Effect of Classroom Environment on Learning Outcomes in Elementary Schools”

Is “Learning Outcomes” a constant or a variable?

A

VARIABLE

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22
Q

variables whose categories are simply used as labels to distinguish one group from another

KEY WORD: CATEGORY

A

QUALITATIVE VARIABLE

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23
Q

numerical representation of the categories are for labeling/coding and not for comparison (greater or less)

KEY WORDS: NUMERICAL, CATEGORY

A

QUALITATIVE VARIABLE

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24
Q

e.g. sex, religion, place of residence, disease status

A

QUALITATIVE VARIABLE

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25
Q

values indicate a quantity or amount and can be expressed numerically

A

QUANTITATIVE VARIABLE

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26
Q

e.g. age, height, weight, blood pressure

A

QUANTITATIVE VARIABLE

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27
Q

[TYPE OF QUANTITATIVE]

can assume only integral values or whole numbers
- aka countable

A

DISCRETE

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28
Q

e.g. number of children in the family, number of beds in the hospital

A

DISCRETE

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29
Q

[TYPE OF QUANTITATIVE]
can attain any value including fractions or decimals
- aka measurable

A

CONTINOUS

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30
Q

[LEVELS OF MEASUREMENT]

it has no order
e.g. sex (male, female), race, blood groups. seatbelts in car, psych diagnosis, patient ID no.

A

NOMINAL

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31
Q

[LEVELS OF MEASUREMENT]
this measurement can be ranked and it has order.
e.g. likert scales, age groups

A

ORDINAL

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32
Q

[LEVELS OF MEASUREMENT]
there is a true zero
- zero is existing
- buhay ang zero
- think of temperature
- think of year date

A

INTERVAL

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33
Q

[LEVELS OF MEASUREMENT]
zero is none
e.g. weight, blood pressure, height, doctor visits, number of DMF teeth
- think of weight

A

RATIO

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34
Q

Extent to which a measurement is consistent and FREE from ERROR.

A

RELIABILITY

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35
Q

[MEASUREMENT ERRORS]

CONSTANT ERROR
- yung error niya is may pattern. consistent error and biased

A

SYSTEMATIC ERROR

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36
Q

reproducibility or dependability

A

RELIABILITY

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37
Q

[MEASUREMENT ERRORS]

ERROR BY CHANCE
- no consistent pattern of errors
- iba iba yung error
- unpredictable variation

A

RANDOM ERROR

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38
Q

During a survey, some participants misunderstand one of the questions, leading to inconsistent responses across different participants.

Is this a systematic error or random error?

A

RANDOM ERROR

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38
Q

In a chemistry lab, a balance is consistently off by 0.5 grams, giving slightly heavier readings for every measurement.

Is this a systematic error or random error?

A

SYSTEMATIC ERROR

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39
Q

A thermometer is improperly calibrated, so it always reads 2°C higher than the actual temperature.

Is this a systematic error or random error?

A

SYSTEMATIC ERROR

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40
Q

In a study, a clock used to time events runs slower than it should, causing all measurements to be off by a consistent amount of time.

Is this a systematic error or random error?

A

SYSTEMATIC ERROR

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40
Q

While measuring the time it takes for runners to finish a race, the stopwatch operator sometimes presses “stop” a little too early or too late, leading to varying times.

Is this a systematic error or random error?

A

RANDOM ERROR

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41
Q

A researcher is using a faulty microphone that occasionally cuts out for short periods of time, causing some of the recorded data to be missing.

Is this a systematic error or random error?

A

RANDOM ERROR

42
Q

In a production factory, all rulers used to measure products are slightly longer than they should be, leading to measurements that are consistently too large.

Is this a systematic error or random error?

A

SYSTEMATIC ERROR

43
Q

An individual’s hand shakes slightly when using a stopwatch, causing minor fluctuations in the recorded time for each trial.

Is this a systematic error or random error?

A

RANDOM ERROR

44
Q

A scientist consistently misreads the scale on a thermometer, causing all temperature readings to be higher than the actual values by 3°C.

Is this a systematic error or random error?

A

SYSTEMATIC ERROR

45
Q

administering the same test over a period of time by the same participant

A

TEST-RETEST

45
Q

While testing different batches of material strength, slight variations in room temperature during each test lead to inconsistent results.

Is this a systematic error or random error?

A

RANDOM ERROR

46
Q

require that a human observer, or rater, be part of the measurement system.

A

RATER RELIABILITY

47
Q

stability of data recorded by one individual across 2 or more trials
(same person each day/week and one responded)

A

INTRARATER or intratester

48
Q

variation between two or more raters who measure the same group of subjects
(different reporter and one client)

A

INTERRATER

49
Q

consistency of scores across different test

A

ALTERNATE FORMS

50
Q

where 2 versions of the instrument exist. Each form will be administered on one occasion and the responses are
compared.

A

ALTERNATE FORMS

51
Q

Consistency of items/content within a single test
“all in the same page”

A

INTERNAL CONSISTENCY/HOMOGEINITY

52
Q

This statistic evaluates the items in a scale to determine if they are measuring the same construct or if they are redundant, suggesting which items could be discarded to improve the homogeneity of the scale.

A

CRONBACH ALPHA

53
Q

appears to test what it is supposed to. WEAKEST form of measurement validity (about impressions—does it seem right?)

A

FACE VALIDITY

54
Q

Claims for content validation are made by a panel of “experts”

A

CONTENT VALIDITY

55
Q

keywords: looks like, seems to measure, appears to

A

FACE VALIDITY

55
Q

Keywords: Predicts, relates to, correlates, corresponds, outcome, performance, comparison, prediction. all about real world performance

A

CRITERION-RELATED VALIDITY

55
Q

Does the test look like it’s measuring what it’s supposed to, based on appearance or judgment?

A

FACE VALIDITY

56
Q

Keywords: Theory, concept, underlying, psychological construct, abstract, aligns with, theoretical foundation.

Quick Description: about theory and abstract concepts

A

CONSTRUCT VALIDITY

56
Q

keywords: Covers all aspects, comprehensive, representative, complete, full range, relevant content.

A

CONTENT RELIABILITY

57
Q

OBSERVE CHANGE IS RELIABLE (NOT DUE TO AN ERROR)

A

Minimally Detectable Change (MDC)

58
Q

OUTCOME IS MEANINGFUL AND BENEFICIAL TO PATIENT

A

Minimally Clinically Important Difference (MCID)

59
Q

CORRECTNESS of measurement

A

ACCURACY

60
Q

Given an individual HAS A DISEASE, the probability that test will be POSITIVE.
- detecting an illness
- all about how well a test finds those who have disease

A

SENSITIVITY

61
Q

CONSISTENCY of measurement occur. there is repeatable measurement

A

PRECISION

62
Q

Given that the individual DOES NOT HAVE A DISEASE, the probability that the test will be NEGATIVE.
- how well a test finds those who do not have the disease
- confirming health
- false alarm

A

SPECIFICITY

63
Q

Describe the primary traits of the target and accessible populations that will QUALIFY someone as a subject.

A

INCLUSION

64
Q

Indicate those factors that would preclude someone from being a subject

A

EXCLUSION CRITERIA

65
Q

larger population - the results from a representative sample can be generalized to this level

A

TARGET POPULATION

66
Q

accessible part of the target population from where the sample is selected

A

STUDY POPULATION

67
Q

This is a list of all the members in the study population

A

SAMPLING FRAME

68
Q

smaller group selected from the population of the study. Also the subset of the population

A

SAMPLE

69
Q

indiv/object that has the characteristics ure studying

A

ELEMENTARY UNIT

70
Q

specific entity selected from or during sampling/sample

A

sampling unity

71
Q

A population is said to be ________ when its every element is similar. In other words, every element has all the characteristics that meet the described criteria of target population.

A

homogenous

72
Q

list/data or your sample

A

sampling frame

73
Q

A population is said to be ________ when its elements are not similar to each other in all aspects.

A

heterogenous

74
Q

what are the 5 types of probability sampling?

A

● Simple Random Sampling
● Systematic Random Sampling
● Stratified Random Sampling
● Cluster Sampling
● Multistage Sampling

75
Q

[TYPE OF PROBABILITY SAMPLING]

Situation: A university wants to survey its students about the quality of campus facilities. They put the names of all students in a database and use a random number generator to select 500 students to participate.

A

simple random sampling

76
Q

[TYPE OF PROBABILITY SAMPLING]

Situation: A factory wants to inspect the quality of the products coming off the assembly line. To do this, they decide to inspect every 10th product after randomly selecting a starting point.

A

Systematic Sampling

77
Q

[TYPE OF PROBABILITY SAMPLING]

Situation: A political researcher wants to understand voting preferences across different age groups. They divide the population into age groups (18-30, 31-50, 51+), then randomly select a proportional number of participants from each group to ensure that all age ranges are represented.

A

Stratified Sampling

78
Q

[TYPE OF PROBABILITY SAMPLING]

Situation: A school district wants to assess the reading levels of students in the district. Instead of testing every student, they randomly select 10 schools (_____) and test all students within those schools.

A

Cluster Sampling

79
Q

[TYPE OF PROBABILITY SAMPLING]

Situation: A national health survey is being conducted. The researchers first randomly select cities (clusters) across the country. Within each selected city, they then randomly select households for interviews.

A

Multistage Sampling

80
Q

[TYPE OF NON-PROBABILITY SAMPLING]

A researcher is studying coffee consumption habits and decides to survey people in a local coffee shop because they are easily accessible and likely to drink coffee.

A

CONVENIENCE SAMPLING

81
Q

[TYPE OF NON-PROBABILITY SAMPLING]

Situation:
“To study the challenges faced by startup founders, the researcher intentionally selects a group of successful entrepreneurs to interview because they have experience and insights directly related to the study.”

Definition: The researcher uses their judgment to select participants based on who they think will provide the most relevant information.

A

Purposive Sampling

82
Q

Situation:
A company is launching a new smartphone and wants feedback from users across different income levels, genders, and geographic locations. The researchers set specific quotas:

  • 100 respondents from low-income households,
  • 100 from middle-income, and 100 from high-income.
  • 50 males and 50 females from each income group.
  • 30 respondents each from urban, suburban, and rural areas in every income and gender group.
A

QUOTA SAMPLING

82
Q

Situation:
“A researcher is studying a rare disease and finds it hard to locate patients. They start by interviewing one patient and ask that patient to refer other patients they know with the same condition.”

A

Snowball Sampling

83
Q

➔ studies as a means of describing the nature and characteristics of the event under investigation.
➔ It is also the initial step in the analysis of data in analytic research.

A

Descriptive Statistics

84
Q

shows the number of times each value occurred

A

FREQUENCY DISTRIBUTION

85
Q

EXAMPLE: (18-22)
➔ constructed by grouping the scores into classes, or intervals, where each class represents a unique range of scores within the distribution
➔ classes are mutually exclusive (no overlap) and exhaust

A

GROUP FREQUENCY DISTRIBUTION

86
Q

a type of bar graph, composed of a series of columns, each representing one score or group interval.

A

HISTOGRAM

87
Q

also called a frequency polygon, shows data point along a contiguous line. When grouped data are used, the points on the line represent the midpoint of each interval

A

LINE PLOT

88
Q

Keywords: Average, sum, central tendency.

The sum of all values divided by the number of values.

A

MEAN

89
Q

like a histogram turned on its side, but with individual values. it is most useful for presenting the pattern of distribution of a continuous variable, derived by separating each score into two parts.

A

STEM-AND-LEAF PLOT

90
Q

Keywords: Middle value, central position, sorted data.

The middle value in a sorted data set (or the average of the two middle values if the set has an even number of values).

A

MEDIAN

91
Q

Keywords: Most frequent, repetition, common value.

The value that appears most frequently in a data set.

A

MODE

92
Q

the left half of its graph (histogram or frequency polygon) will be a mirror image of its right half.

A

SYMMETRIC

93
Q

➔ Positively skewed
➔ Negatively skewed

A

ASYMMETRIC

94
Q

Keywords: Spread, difference, highest minus lowest.

The difference between the highest and lowest values in the data set

Range=Max value−Minimumvalue

A

RANGE

95
Q

Keywords: Dispersion, deviation from mean, squared differences. the average of squared differences from the mean. It is simply the squared value of the standard deviation

A measure of how much the values in a data set deviate from the mean.

A

VARIANCE

96
Q

The square root of the variance, giving a measure of the spread of data points around the mean in the same units as the data.
➔ Square root of the variance

A

Standard Deviation (SD)

97
Q

The ratio of the standard deviation to the mean, expressed as a percentage. It shows the relative variability of data, useful for comparing data sets with different units or scales.

A

COEFFICIENT OF VARIATIONS

97
Q

values of random variable X that divides the observations into 100 equal parts

A

PERCENTILE

98
Q

values of random variable X that divides the observations into 10 equal parts

A

DECILE

99
Q

values of random variable X that divides the observation into 4 equal parts

A

QUARTILE

100
Q

distribution is “peaked” or flat in comparison

A

KURTOSIS

101
Q

kurtosis is negative

A

PLATYKURTIC

102
Q

kurtosis is positive

A

LEPTOKURTIC

103
Q

➔ normal kurtosis

A

MESOKURTIC

SECOND YR (7 decks)

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