BIOSTATS [PRELIMS] Flashcards
a set of data or a mass of observations
STATISTICS
collection, organization, analysis and interpretation of numerical data
STATISTICS
Application of statistics to biological problems
BIOSTATISTICS
vital statistics, health statistics
STATISTICS
tendency of a measurable
characteristics to change with respect to person, place & time e.g. weight, age, height, etc.
VARIATION
statistical techniques for summarizing
DESCRIPTIVE STATISTICS
Present data in a form that will make them easier to ANALYZE and INTERPRET
Descriptive Data
counts, proportions, tables, graphs, etc.
DESCRIPTIVE DATA
estimates, predictions, generalizations and conclusions about a target population
INFERENTIAL STATISTICS
TWO TYPES OF INFERENTIAL (key components in distinguishing the branch of inferential)
a. estimation
b. hypothesis testing
it measures and count statistics
data
raw material of statistics
data
(hindi nababago) fix or unchanging measurements / phenomenon
CONSTANT
(nababago and inconsistent) changing or inconsistent measurements cannot be predicted with certainty
VARIABLE
Title: “The Effect of Sleep Duration on Academic Performance among High School Students”
Is “Sleep Duration” a constant or a variable?
VARIABLE
Title: “The Relationship Between Income Level and Healthcare Access in Urban Areas”
Is “Income Level” a constant or a variable?
VARIABLE
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?
CONSTANT
In a study, all participants are given the same questionnaire with identical questions.
Is the “Questionnaire” a constant or a variable?
CONSTANT
Title: “The Impact of Exercise Frequency on Mental Health in College Students”
Is “College Students” a constant or a variable?
CONSTANT
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?
CONSTANT
Title: “The Effect of Study Habits on Grades among University Freshmen”
Is “Grades” a constant or a variable?
VARIABLE
Title: “The Relationship Between Social Media Use and Self-Esteem in Adolescents”
Is “Self-Esteem” a constant or a variable?
VARIABLE
In a survey, participants are all aged 30 years old.
Is “Age” a constant or a variable?
CONSTANT
Title: “The Effect of Classroom Environment on Learning Outcomes in Elementary Schools”
Is “Learning Outcomes” a constant or a variable?
VARIABLE
variables whose categories are simply used as labels to distinguish one group from another
KEY WORD: CATEGORY
QUALITATIVE VARIABLE
numerical representation of the categories are for labeling/coding and not for comparison (greater or less)
KEY WORDS: NUMERICAL, CATEGORY
QUALITATIVE VARIABLE
e.g. sex, religion, place of residence, disease status
QUALITATIVE VARIABLE
values indicate a quantity or amount and can be expressed numerically
QUANTITATIVE VARIABLE
e.g. age, height, weight, blood pressure
QUANTITATIVE VARIABLE
[TYPE OF QUANTITATIVE]
can assume only integral values or whole numbers
- aka countable
DISCRETE
e.g. number of children in the family, number of beds in the hospital
DISCRETE
[TYPE OF QUANTITATIVE]
can attain any value including fractions or decimals
- aka measurable
CONTINOUS
[LEVELS OF MEASUREMENT]
it has no order
e.g. sex (male, female), race, blood groups. seatbelts in car, psych diagnosis, patient ID no.
NOMINAL
[LEVELS OF MEASUREMENT]
this measurement can be ranked and it has order.
e.g. likert scales, age groups
ORDINAL
[LEVELS OF MEASUREMENT]
there is a true zero
- zero is existing
- buhay ang zero
- think of temperature
- think of year date
INTERVAL
[LEVELS OF MEASUREMENT]
zero is none
e.g. weight, blood pressure, height, doctor visits, number of DMF teeth
- think of weight
RATIO
Extent to which a measurement is consistent and FREE from ERROR.
RELIABILITY
[MEASUREMENT ERRORS]
CONSTANT ERROR
- yung error niya is may pattern. consistent error and biased
SYSTEMATIC ERROR
reproducibility or dependability
RELIABILITY
[MEASUREMENT ERRORS]
ERROR BY CHANCE
- no consistent pattern of errors
- iba iba yung error
- unpredictable variation
RANDOM ERROR
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?
RANDOM ERROR
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?
SYSTEMATIC ERROR
A thermometer is improperly calibrated, so it always reads 2°C higher than the actual temperature.
Is this a systematic error or random error?
SYSTEMATIC ERROR
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?
SYSTEMATIC ERROR
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?
RANDOM ERROR
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?
RANDOM ERROR
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?
SYSTEMATIC ERROR
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?
RANDOM ERROR
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?
SYSTEMATIC ERROR
administering the same test over a period of time by the same participant
TEST-RETEST
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?
RANDOM ERROR
require that a human observer, or rater, be part of the measurement system.
RATER RELIABILITY
stability of data recorded by one individual across 2 or more trials
(same person each day/week and one responded)
INTRARATER or intratester
variation between two or more raters who measure the same group of subjects
(different reporter and one client)
INTERRATER
consistency of scores across different test
ALTERNATE FORMS
where 2 versions of the instrument exist. Each form will be administered on one occasion and the responses are
compared.
ALTERNATE FORMS
Consistency of items/content within a single test
“all in the same page”
INTERNAL CONSISTENCY/HOMOGEINITY
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.
CRONBACH ALPHA
appears to test what it is supposed to. WEAKEST form of measurement validity (about impressions—does it seem right?)
FACE VALIDITY
Claims for content validation are made by a panel of “experts”
CONTENT VALIDITY
keywords: looks like, seems to measure, appears to
FACE VALIDITY
Keywords: Predicts, relates to, correlates, corresponds, outcome, performance, comparison, prediction. all about real world performance
CRITERION-RELATED VALIDITY
Does the test look like it’s measuring what it’s supposed to, based on appearance or judgment?
FACE VALIDITY
Keywords: Theory, concept, underlying, psychological construct, abstract, aligns with, theoretical foundation.
Quick Description: about theory and abstract concepts
CONSTRUCT VALIDITY
keywords: Covers all aspects, comprehensive, representative, complete, full range, relevant content.
CONTENT RELIABILITY
OBSERVE CHANGE IS RELIABLE (NOT DUE TO AN ERROR)
Minimally Detectable Change (MDC)
OUTCOME IS MEANINGFUL AND BENEFICIAL TO PATIENT
Minimally Clinically Important Difference (MCID)
CORRECTNESS of measurement
ACCURACY
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
SENSITIVITY
CONSISTENCY of measurement occur. there is repeatable measurement
PRECISION
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
SPECIFICITY
Describe the primary traits of the target and accessible populations that will QUALIFY someone as a subject.
INCLUSION
Indicate those factors that would preclude someone from being a subject
EXCLUSION CRITERIA
larger population - the results from a representative sample can be generalized to this level
TARGET POPULATION
accessible part of the target population from where the sample is selected
STUDY POPULATION
This is a list of all the members in the study population
SAMPLING FRAME
smaller group selected from the population of the study. Also the subset of the population
SAMPLE
indiv/object that has the characteristics ure studying
ELEMENTARY UNIT
specific entity selected from or during sampling/sample
sampling unity
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.
homogenous
list/data or your sample
sampling frame
A population is said to be ________ when its elements are not similar to each other in all aspects.
heterogenous
what are the 5 types of probability sampling?
● Simple Random Sampling
● Systematic Random Sampling
● Stratified Random Sampling
● Cluster Sampling
● Multistage Sampling
[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.
simple random sampling
[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.
Systematic Sampling
[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.
Stratified Sampling
[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.
Cluster Sampling
[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.
Multistage Sampling
[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.
CONVENIENCE SAMPLING
[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.
Purposive Sampling
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.
QUOTA SAMPLING
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.”
Snowball Sampling
➔ 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.
Descriptive Statistics
shows the number of times each value occurred
FREQUENCY DISTRIBUTION
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
GROUP FREQUENCY DISTRIBUTION
a type of bar graph, composed of a series of columns, each representing one score or group interval.
HISTOGRAM
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
LINE PLOT
Keywords: Average, sum, central tendency.
The sum of all values divided by the number of values.
MEAN
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.
STEM-AND-LEAF PLOT
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).
MEDIAN
Keywords: Most frequent, repetition, common value.
The value that appears most frequently in a data set.
MODE
the left half of its graph (histogram or frequency polygon) will be a mirror image of its right half.
SYMMETRIC
➔ Positively skewed
➔ Negatively skewed
ASYMMETRIC
Keywords: Spread, difference, highest minus lowest.
The difference between the highest and lowest values in the data set
Range=Max value−Minimumvalue
RANGE
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.
VARIANCE
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
Standard Deviation (SD)
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.
COEFFICIENT OF VARIATIONS
values of random variable X that divides the observations into 100 equal parts
PERCENTILE
values of random variable X that divides the observations into 10 equal parts
DECILE
values of random variable X that divides the observation into 4 equal parts
QUARTILE
distribution is “peaked” or flat in comparison
KURTOSIS
kurtosis is negative
PLATYKURTIC
kurtosis is positive
LEPTOKURTIC
➔ normal kurtosis
MESOKURTIC
