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Liu Qin: Observation Study--A study based on data in which no manipulation of factors has been employed.

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Liu Qin: Observation Study--A study based on data in which no manipulation of factors has been employed.

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Experimental study is a study with treatment where researchers use treatment to affect an independent variable and observe the effect. *The subjects are randomly assigned to the treatments.

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A retrospective study looks backward in time, examining historical data and events that have already occurred. These studies typically rely on medical records, administrative databases, or self-reported information to assess the association between an exposure and an outcome.

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Simple random sample: each combination of people has an equal chance of being selected

Sampling frame: a list of individuals from which the sample is drawn

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A simulation models a real world situation by using random digit outcomes to mimic the uncertainty of a response variable of interest.

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selecting a few clusters that are representative of the population and performing a census within them is called a cluster sampling

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Your trial's outcomes are called the response variable

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Prospective Study — a type of observational study that follows participants forward in time, observing outcomes as they occur.

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A sampling design in which the population is divided into several subpopulations, or strata, and random samples are then drawn from each stratum. If the strata are homogeneous, but are different from each other, a stratified sample may yield more consistent results than an SRS.

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When designing an experiment, random assignment can equalize the effects of all unknown variables. For known variables, we can use blocking to eliminate variability due to difference among blocks.

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A parameter used in a model for a population is sometimes called (redundantly) a population parameter. Any summary found from the data is a statistic. Sometimes you’ll see the (also redundant) term sample statistic.

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When the confounding variable is known then use blocking. If unknown variable then randomize to equalize the effect on subject.

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those who could influence the results (e.g. the subjects, technicians)

those who evaluate the results (e.g. judges, treating physicians)

When one of the two groups are blinded -> single-blind

When both off the two groups are blinded -> double blind

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In observational studies, researchers don’t assign choices; they simply observe them. In addition, retrospective study is a type of observational study, in which researchers first identify subjects and then collect data on their past progress.

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Voluntary response sample often have rsponse bias

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randomized, double-blind, comparative, placebo-controlled

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Undercoverage is common in survey designs, it can cause some part of the sample to be not sampled. Ex: cafeteria example

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There are two ways of replication. One is using more subjects in one sample. Experiment on one subject is an anecdote, not data. Another way is to repeat the experiment with controlled variables at a different levels.

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statistical significance in a well-designed experiment suggest a causation relationship but for observational study a causation cannot be concluded

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A component uses equally likely random digits to model simple random occurrences whose outcomes may not be equally likely.

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Randomizing protects us from the influences of all the features of our population by making sure that, on average, the sample looks like the rest of the population.

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1. Control

2. Randomize

3. Replicate

4. Blocking

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A placebo is a treatment that's known to have no effect, used so that the groups experience same conditions

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Including everyone in the population is claled a CENSUS

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A point in a scatter plot is said to have a high leverage if its x-value is far from the mean of the x-value. A point is influential if omitting it from the analysis gives a very different model.(A high leverage is an outlier, but an outlier does not necessarily have a high leverage. Both a point has high leverage and an outlier is influential)

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For answers that include biases, identify the bias is possible, then explain why the sample is not representative, and talk about the direction of response variable that the bias affects.

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When the levels of one factor are associated with the levels of another factor, we say that these two factors are confounded.

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Statistics are values calculated for sampled data. Those what correspond to, and thus estimate, a population parameter, are of particular interest. For example, the mean income of all employed people in a representative sample can provide a good estimate of the corresponding population parameter. The term "sample statistic" is sometimes used, usually to parallel the corresponding term "population parameter"

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The sequence of events we want to investigate is called a TRIAL

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Extrapolation: A skeptical prediction of a value since the x value is too far away from the mean x. It requires the assumption that nothing about the relationship between x and y changes even at extreme value of x and beyond, which is very easy to be wrong.

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I agree

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A LURKING VARIABLE is associated with both x and y in such a way that it appears that x may be causing y.

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Multistage sampling is a method of obtaining a sample from a population by splitting a population into smaller and smaller groups and taking samples of individuals from the smallest resulting groups.

For example, we may take a simple random sample of 15 states in America. Then, within each state we may take a simple random sample of 10 counties.Then, within each county we may take a simple random sample of 100 households.

This method of obtaining this sample is known as multistage sampling.