Data and Sampling

Flashcards covering key concepts in data analysis, sampling, and statistical visualization.

Statistical Thinking

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.

Population vs Sample

Distinction between the entire group of interest (population) and a subset of that group (sample).

Estimate vs Statistic vs Parameter

An estimate is a value derived from sample data, while a statistic refers to a numerical characteristic of a sample, and a parameter is a numerical characteristic of a population.

Deviation in Estimation

The difference between an estimated value and the actual value.

Representative Sample

A sample that accurately reflects the characteristics of the population as a whole.

Importance of Good Data

Good data is crucial for making informed decisions and conclusions in statistical analysis.

Mean

The average value of a set of numbers, calculated by summing all values and dividing by the number of values.

Standard Deviation

A measure that indicates the amount of variation or dispersion in a set of values.

Sampling Error

The error caused by observing a sample instead of the whole population.

Bias

A systematic error that leads to an incorrect estimation.

Exploratory Data Visualization

Using visual methods to explore and analyze data before formal analysis.

Explanatory Data Visualization

Creating visual representations to communicate results and insights derived from data analysis.

Histograms

A graphical representation that organizes a group of data points into specified ranges.

Density Plots

A smoothed version of a histogram that shows the distribution of a variable.

Mean of Squared Differences

The average of the squares of the differences between individual data points and the mean.

Common Biases in Sampling

Includes sampling error, measurement error, and selection bias.

Data Visualization Best Practices

Guidelines that help present data effectively and clearly.