Data Production Notes #1

Data Production: Take sample data from the population, with sampling and study designs that avoid bias.

Displaying and Summarizing: Use appropriate displays and summaries of the sample data, according to variable types and roles.

Probability: Assume we know what's true for the population; how should random samples behave?

Statistical Inference: Assume we only know what's true about sampled values of a single variable or relationship: what can we infer about the larger population?

Population: The entire collection of individuals ypu want to learn about.

(Parameter) is the numerical value

Sample: Part of the population thsat is selected for the study

(Statistic) is the numerical value

Experiment: A study where the person conducting th study considres how a reepsonse behaves under experimental conditions.

Observatory study: A study where the person conductiong observes characteristics of a sample selcted from a population.

Hawthorne Effect: Noticing that employees work harder simply ecause management installed a camera and they know that they are being watched.

Anecdotal Evience: A friend claims that drinking herba ea cured their cold- butthats’s just one story, not scientific proof.

Confounding Variables: Finding that children who wear glasses tend to do better in school, without realizing that it's age (not glasses) influencing both

Lack of Realism: Testing the effect of loud music on concentration in a quiet lab maynot represent a stoudent’s noisy home environment..

Paired Sample: Measuring students test scores before and after a special tutoting session to see improvement.

Two Sample: Comparing average heights between tw independent groups of men and women.

Type 1 Error: A COVID-19 test says “positive” when the patient is actually healthy ( a false alarm).

Type 2 Error: A cancer screenint test says “negative” when the patient actually has cancer— a missed diagnosis.

                                                                Types of samples

                                                            Avoiding Bias