INTRO Flashcards by Kyra Catedral

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

Statistics

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

Statistics

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

Biostatistics

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subcategory of statistics, it is statistics applied to biology

Biostatistics

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

Variable

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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

Random Variable

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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

Non-random Variable

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

Data

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collection of data values
table of data

Data

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each value in a data set
individual values

Data value or datum

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consists of all the subjects that fits the criteria

Population

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group of subjects selected from the population

Sample

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is a decision making process for evaluating claims about a population

Hypothesis testing

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

Descriptive Statistics

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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

Descriptive Statistics

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EXAMPLE:
Male - 51%
Female - 49%

Descriptive Statistics

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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.

Inferential Statistics

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performing estimations and hypothesis tests;

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

Inferential Statistics

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determining relationships among variables; and

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

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making predictions

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

describing and drawing conclusions from a given data

Inferential Statistics

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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

Qualitative Variable

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are numerical and can be ordered or ranked

e.g. age, grades, blood glucose level

Quantitative Variable

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Quantitative Variable: 2 types

discrete variables
continuous variables

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

discrete variables

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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

continuous variables

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

continuous variables

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

continuous data

Measurement Scales

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

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

Nominal Level of Measurement

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

Nominal Level of Measurement

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

it is simply for naming or categorical purposes

Nominal Level of Measurement

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

Ordinal Level of Measurement

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

Ordinal Level of Measurement

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)

Ordinal Level of Measurement

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.

Ordinal Level of Measurement

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

Interval Level of Measurement

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

Interval Level of Measurement

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

Interval Level of Measurement

e.g lowest grade cannot be 0

Interval Level of Measurement

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

Interval Level of Measurement

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

Ratio Level of Measurement

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

Ratio Level of Measurement

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)

Ratio Level of Measurement

Temperature in celsius or fahrenheit falls under ________

interval-level

Kelvin falls under _________ as kelvin starts at 0.

ratio-level

Interval and Ratio falls under ____________

Numerical or Quantitative Data

3 types of data collection

Survey Surveying records Direct observations

PROBABILISTIC SAMPLING

Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling

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

Random Sampling

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

Systematic Sampling

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.

Stratified Sampling

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

Cluster Sampling

NON PROBABILISTIC SAMPLING

Convenience Sampling Voluntary response Sampling Snowball Sampling

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

Convenience Sampling

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

Voluntary response Sampling

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

Snowball Sampling

Statistical Studies: 2 types

Observational Study Experimental Study

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

Observational Study

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

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

Observational Study

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

Experimental Study

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

Experimental Study

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

Experimental Study

Variables in Statistical Studies: 3

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

the variable being manipulated by the researcher

Independent Variable (Explanatory Variable)

dependent on the independent variable resultant variable heavily affected by the independent variable

Dependent Variable (Outcome Variable)

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

Confounding Variable

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

Suspect Samples

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

Ambiguous Averages

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

Changing The Subject

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

Detached Statistics

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

Implied Connections

no sense of numbers or labels in the axis

Misleading Graphs

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

Faulty Survey Questions