In this article, covering fundamental statistical terms and their meanings with relatable examples.
- Qualitative: These data describe qualities or characteristics, such as the colour of cars on a street.
- Quantitative: On the other hand, quantitative data involve numerical measurements, such as the number of cars on a street.
- Nominal: Categories like religion or political party affiliation fall under nominal data, lacking inherent order or ranking.
- Ordinal: Education levels represent ordinal data, featuring a natural order.
- Interval: Temperature measurements adhere to interval data, featuring equal intervals between points but no true zero.
- Ratio: Parameters such as income or population size constitute ratio data, featuring equal intervals and a true zero point.
- Sample: Consider measuring the heights of 100 students from different schools to estimate the average height. These 100 students form our sample.
- Population: On the other hand, the population would encompass all students, representing the entire group of interest.
- Parameter: When determining the average income of all families, the resulting figure is a parameter, a numerical measure describing a characteristic of the population.
- Statistic: Conversely, if we compute the average income of 100 families as a representative sample, this figure serves as a statistic, reflecting a characteristic of the sample.
- Descriptive Statistics: These summarize key features of a dataset, such as median age or wealth distribution, providing a snapshot of the demographic and economic landscape.
- Inferential Statistics: By utilizing data from a sample, we can make predictions or inferences about the broader population, like forecasting election outcomes based on voter trends.
