Textbook - Statistical Thinking

these 2 variables reveal 2 fundamental aspects of statistical thinking

- data vary
- analyzing patterns of variables often reveal light

statistical significance

A result is statistically significant if it is unlikely to arise by chance alone.

key components to a statistical investigation

1. Planning the study - ask testable research questions and decide how to collect data
2. Examining the data - patterns in graph, what is relevant
3. Inferring from the data - what are valid statistical methods for drawing inferences beyond data collected
4. Drawing conclusions

cause and effect

one variable causes changes in the other variable

distribution

pattern of variation in data

p-value

probability of observing a particular outcome in a sample
tells you how often a random process would give a result at least as extreme as what's found in study (ex: used for what are the chances of getting heads 16/16 times when flipping a coin)

sample

The collection of individuals on which we collect data.

population

larger collection of individuals we generalize our results to

generalizability

related to whether the results from sample can be generalized to larger population

random sampling

probability base method to select subset of individuals for sampole

margin of error

The expected amount of random variation in a statistic; often defined for 95% confidence level.

margin of error used for

to claim how often the sample result would fall within a certain distance from unknown population value by chance