BIOSTATS 100A EXAM QUESTIONS AND ACCURATE ANSWERS 2024/2025 |
VERIFIED
Blinded study - ✔️✔️Study consisting of individuals randomized for treatment or placebo groups; both
the participant and the administrator do not know who is getting what
Statistical process - ✔️✔️Examples include clinical research studies and opinion polls
Classifying Data - ✔️✔️Data may be classified as quantitative or qualitative
Quantitative Data - ✔️✔️Measurable data; something you can assign a number to that is meaningful;
can be broken down into continuous or discrete
Continuous Quantitative Data - ✔️✔️Something that literally covers the entire line; there is a lower and
upper bound, and any values in between can be significant; includes values such as height and age
Discrete Quantitative Data - ✔️✔️Certain distinct values, such as heart rate
Qualitative Data - ✔️✔️Something that is categorical in nature, and thus cannot be assigned a number,
such as gender or eye color or disease
Ordinal data - ✔️✔️Data that has ordinality, a gray area between quantitative and qualitative data;
numerical data with arbitrary numbers which are only significant when order is taken into account, such
as the visual analog scale in response to an analgesic; AKA semi-quantitative data; part of the Steven's
scale
Steven's Scale - ✔️✔️Classifying data more thoroughly by taking into account arithmetic; includes
nominal data, ordinal data, interval data, and ratio data
Nominal data - ✔️✔️No arithmetic is possible with type of data; part of the Steven's scale
Interval data - ✔️✔️Can do ordering, add, and subtract with this type of data; part of the Steven's scale
, Ratio data - ✔️✔️Data which can be multiplied, added, subtracted, and divided; part of the Steven's
scale
Sampling - ✔️✔️Taking a group which may be representative of the population; includes probability and
non-probability sampling
Probability Sampling - ✔️✔️Every element of the population has a probability of being chosen, such
that selection of the sample is strictly due to the laws of probability, meaning randomness and
uncertainty; types include simple random sampling, which sometimes includes block randomization,
stratified random sampling, cluster random sampling, and systematic random sampling
Non-probability Sampling - ✔️✔️Some elements of the population have no chance/probability of being
chosen; people chosen for sample may have been convenient, but were certainly not chosen based on
each object having equal probability of being chosen
Self-selection bias - ✔️✔️A type of clinical research trial wherein participation by consent is used;
because of this, only looking at small fraction of population; this method is used constantly, and is used
to make inferences and develop treatments, as there is no way to get out of this dilemma
Simple Random Sampling - ✔️✔️Everyone has equal probability, such as lottery picking; first must
establish the population frame which describes/IDs each person in the population; unique identifiers are
used and random number generator ensures equal probability of each to be chosen
Block randomization - ✔️✔️When forming groups but wanting to preserve randomization/probability to
attain SRS, can use this technique to ensure even numbers of individuals in each group randomly; even
numbers of individuals are balanced each step by picking a block of specific size; using randomized
permuted blocks, every 4 individuals which enter the study will be evenly divided into each group
Homogeneous Population - ✔️✔️Population that is uniform, where everything has the same
probability, and a SRS can be taken from the population
Heterogeneous Population - ✔️✔️Population in which SRS can fail as the population is not entirely
uniform, and instead various groups known as strata arise; instead, stratified random sampling must be
used
VERIFIED
Blinded study - ✔️✔️Study consisting of individuals randomized for treatment or placebo groups; both
the participant and the administrator do not know who is getting what
Statistical process - ✔️✔️Examples include clinical research studies and opinion polls
Classifying Data - ✔️✔️Data may be classified as quantitative or qualitative
Quantitative Data - ✔️✔️Measurable data; something you can assign a number to that is meaningful;
can be broken down into continuous or discrete
Continuous Quantitative Data - ✔️✔️Something that literally covers the entire line; there is a lower and
upper bound, and any values in between can be significant; includes values such as height and age
Discrete Quantitative Data - ✔️✔️Certain distinct values, such as heart rate
Qualitative Data - ✔️✔️Something that is categorical in nature, and thus cannot be assigned a number,
such as gender or eye color or disease
Ordinal data - ✔️✔️Data that has ordinality, a gray area between quantitative and qualitative data;
numerical data with arbitrary numbers which are only significant when order is taken into account, such
as the visual analog scale in response to an analgesic; AKA semi-quantitative data; part of the Steven's
scale
Steven's Scale - ✔️✔️Classifying data more thoroughly by taking into account arithmetic; includes
nominal data, ordinal data, interval data, and ratio data
Nominal data - ✔️✔️No arithmetic is possible with type of data; part of the Steven's scale
Interval data - ✔️✔️Can do ordering, add, and subtract with this type of data; part of the Steven's scale
, Ratio data - ✔️✔️Data which can be multiplied, added, subtracted, and divided; part of the Steven's
scale
Sampling - ✔️✔️Taking a group which may be representative of the population; includes probability and
non-probability sampling
Probability Sampling - ✔️✔️Every element of the population has a probability of being chosen, such
that selection of the sample is strictly due to the laws of probability, meaning randomness and
uncertainty; types include simple random sampling, which sometimes includes block randomization,
stratified random sampling, cluster random sampling, and systematic random sampling
Non-probability Sampling - ✔️✔️Some elements of the population have no chance/probability of being
chosen; people chosen for sample may have been convenient, but were certainly not chosen based on
each object having equal probability of being chosen
Self-selection bias - ✔️✔️A type of clinical research trial wherein participation by consent is used;
because of this, only looking at small fraction of population; this method is used constantly, and is used
to make inferences and develop treatments, as there is no way to get out of this dilemma
Simple Random Sampling - ✔️✔️Everyone has equal probability, such as lottery picking; first must
establish the population frame which describes/IDs each person in the population; unique identifiers are
used and random number generator ensures equal probability of each to be chosen
Block randomization - ✔️✔️When forming groups but wanting to preserve randomization/probability to
attain SRS, can use this technique to ensure even numbers of individuals in each group randomly; even
numbers of individuals are balanced each step by picking a block of specific size; using randomized
permuted blocks, every 4 individuals which enter the study will be evenly divided into each group
Homogeneous Population - ✔️✔️Population that is uniform, where everything has the same
probability, and a SRS can be taken from the population
Heterogeneous Population - ✔️✔️Population in which SRS can fail as the population is not entirely
uniform, and instead various groups known as strata arise; instead, stratified random sampling must be
used
