Flashcard library · Statistics
Statistics: Key Concepts & Definitions
Master essential statistical concepts and definitions with this comprehensive flashcard deck. Perfect for students looking to reinforce core knowledge and prepare for exams, these cards cover fundamental terms from descriptive statistics to inferential methods, ensuring a solid understanding of the basics. Dive in to solidify your grasp on the language of data analysis.
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What is the difference between a population and a sample?
The population is the whole group of interest; a sample is the subset actually studied.
What is the difference between a parameter and a statistic?
A parameter describes a population; a statistic is computed from a sample to estimate it.
What is the difference between categorical and quantitative data?
Categorical data puts cases into groups; quantitative data are measured or counted numbers.
What are the four levels of measurement?
Nominal, ordinal, interval and ratio.
What distinguishes ratio data from interval data?
Ratio data has a true zero, so ratios like "twice as much" make sense.
What do descriptive statistics do?
Summarize and display data, for example with means and standard deviations.
What do inferential statistics do?
Use sample data to draw conclusions about a population.
What are the three measures of center?
Mean (average), median (middle value) and mode (most frequent value).
What is standard deviation?
The typical distance of values from the mean — the square root of the variance.
What are the key features of a normal distribution?
Symmetric and bell-shaped, with mean, median and mode equal.
What does the central limit theorem say?
For large enough samples, the sampling distribution of the mean is approximately normal, whatever the population's shape.
What is the null hypothesis?
The statement of no effect or no difference that a test tries to find evidence against.
What is a p-value?
The probability of results at least as extreme as those observed, assuming the null hypothesis is true.
What does a 95% confidence interval mean?
The method captures the true parameter in about 95% of repeated samples.
What is a Type I error?
Rejecting a null hypothesis that is actually true (a false positive).
What is a Type II error?
Failing to reject a null hypothesis that is actually false (a false negative).
What is sampling bias?
A sample that systematically differs from the population, skewing results.