How Collected Data Tells The Truth - AP Statistics

An experiment where neither the subjects nor the experimenters know who receives the treatment. Prevents bias from expectations of both subjects and researchers.

Naturalistic observation. Observing in natural settings without researcher intervention.

The difference between a sample statistic and the corresponding population parameter. Natural variation between sample results and true population values.

A complete enumeration of a population. Attempts to collect data from every individual in the population.

Random variable. Its value changes randomly according to some probability distribution.

Histogram. Shows frequency patterns using bars of different heights.

A summary of how often different values occur in a dataset. Shows how frequently each value appears in the data.

To summarize and describe the main features of a dataset. Organizes and presents data patterns without making predictions.

Making predictions or inferences about a population based on a sample. Uses sample data to draw conclusions about the larger population.

Data that describes qualities or characteristics. Describes categories or attributes that can't be measured numerically.

Data that can be measured and expressed numerically. Represents measurable quantities with numerical values.

To ensure results are consistent and not due to chance. Repeated trials help distinguish real effects from random variation.

The extent to which the results of a study can be generalized to other situations. Measures how well findings apply beyond the specific study conditions.

The difference between the first and third quartiles in a dataset. Measures the spread of the middle $50%$ of data values.

A study where conditions are controlled to determine cause and effect. Researchers actively manipulate variables to test causation.

Observational study. Researchers watch and record but don't manipulate variables.

Bias from individuals who self-select into the survey. Self-selected participants often have strong opinions, creating bias.

Longitudinal data. Tracks changes in the same subjects over time.

A variable that influences both the dependent and independent variables. Makes it hard to determine which variable causes the effect.

To serve as a baseline for comparison in an experiment. Shows what happens without the treatment for comparison.

Bias introduced by anything in a survey design that influences responses. Survey design itself affects how people answer questions.

Bias introduced when a large fraction of sampled individuals fail to respond. Missing responses can skew results if non-responders differ systematically.

Selecting every $k^{th}$ individual from a list of the population. Uses a regular interval pattern to choose subjects systematically.

A sample that divides the population into strata and samples each stratum. Groups similar individuals together, then samples from each group.

Each member of the population has an equal chance of selection. No individual is more likely to be chosen than another.

To reduce bias and ensure each member has an equal chance of selection. Randomness prevents systematic selection patterns that create bias.

Cross-sectional data. Like a snapshot - captures one moment in time.

A numerical summary of a population. It's a fixed value describing the entire population.

A numerical summary of a sample. It describes characteristics of the sample data collected.