Statistics on-ramps unit 1 vocab

Numerical

has numbers

Discrete date

CAN be counted is whole and separate (no halves or decimals)

Continuous

Any number, including fractions or decimals

Categorical data

Grouping things into categories or labels rather than using numbers, it describes characteristics

Nominal data

Each category is just a name you can’t put it in any order or compare them beyond the fact they are different

Oridinal data

Categories have meaningful order or ranking example small medium, large or letter grades

Population data

Collection of all outcomes example; if we want to know if a drug for adults, who get headaches is effective, the population is every adult who gets headaches ever

Samples

Subset or part of a population

Descriptive statistics

This is statistics that just explains what we see in the sample(uses numerical and graphing methods)

Inferential statistics

Using a sample and with well collected data to make an inference about the population

Inference

Well rationed science-based educated guess about the population

Sampling error

refers to the difference between a sample statistic (such as the sample mean) and the actual population parameter it is intended to estimate (such as the population mean). This error occurs because the sample is only a subset of the population and may not perfectly represent it.

Bias

bias occurs when a sample is not representative of the population, leading to systematic errors in results. This happens because some members of the population have a higher or lower chance of being included in the sample. Bias reduces the accuracy of the conclusions drawn from the sample. Reducing bias requires careful sampling methods, like using random sampling.. ( not representative of population)

Confounding/lurking variables

Hidden factor that isn’t included in your study, but can still influence the variables

Convenience sampling

When you pick people for a study because they are easy to reach

Simple, random sampling

Everyone has an equal chance/ representative of population(for example, drawing names from a hat)

Stratified random sampling

When you first divide the population into groups then randomly pick people from each group

Bias

Systematic error in sampling

Structure data set

data that is organized into a clear format, often in tables or spreadsheets, where each row represents an observation (like a person or an event) and each column represents a variable (like age, income, or test scores). Structured data sets have defined relationships between the data points and can easily be analyzed using statistical methods.

Quantitive

Equals numbers, something you can count or measure

Variables

Things that can change

Parameter of interest

proportion of population that exhibits a certain characteristic or behavior

Statistic(multiple choice)

The result you Get from your sample