AFDA U1D3 vocab

Ratio Data

• Orders measurements like nominal data, but has a real zero (e.g., temperature). Zero means none!

• Orders measurements like interval data, but has a real zero (e.g., age). Zero means none!

• Orders measurements like ordinal data, but has a real zero (e.g., weight). Zero means none!

• Orders measurements like interval data, but does not have a real zero (e.g., IQ). Zero means none!

Observational Study

• A researcher conducts experiments to test a hypothesis.

• A researcher observes and records data (just watches).

• A researcher manipulates variables to determine cause and effect.

• A researcher surveys participants to gather opinions.

Interval Data

• Orders measurements with equal intervals (e.g., IQ score, SAT score).

• Categorizes data without a true zero point (e.g., temperature in Celsius).

• Ranks data in a specific order but does not have equal intervals (e.g., race positions).

• Groups data into categories without any order (e.g., types of fruit).

Instrumental Bias

• Happens when instruments are not programmed correctly.

• Occurs when data is misinterpreted due to external factors.

• Results from the use of outdated technology in measurements.

• Is caused by human error in data collection.

Statistical Bias

• Errors in the data cycle.

• Accurate data representation.

• Random sampling techniques.

• Data normalization processes.

Census

• A survey conducted every year to assess economic growth.

• A count or measure of the entire population.

• A method of sampling a small group to represent a larger population.

• A statistical analysis of demographic trends.

Ordinal Data

• Categories without order or rank (e.g., colors, names).

• Categories with order or rank (e.g., 1st place, 2nd place, 3rd place).

• Categories that can be measured numerically (e.g., height, weight).

• Categories that are mutually exclusive (e.g., male, female).

Reliability

• The ability to produce different outcomes each time.

• The consistency of the result (the same outcome over and over).

• The degree to which a test measures what it claims to measure.

• The extent to which results can be generalized to other settings.

Nominal Data

• Categorized by names (e.g., phone brand, color M&M).

• Measured on a numerical scale (e.g., height, weight).

• Ordered categories with a meaningful sequence (e.g., rankings).

• Data that can be divided into intervals (e.g., temperature).

Bias Sample

• A sample that represents the entire population accurately.

• One or more parts of the population are favored over the others.

• A sample that is randomly selected from the population.

• A sample that includes only the most extreme cases.