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Parameter, Statistics, and Statistical Inference

Parameter: a number that is a summary characteristic of a population

Population Parameter: Makes it clear that the parameter is associated with a population, not a sample

Statstic: AKA sample Statistic

Sample Statistic: a number computed from sample values taken from a larger population

Estimate: AKA sample estimate

Sample Estimate: when the statistic is used to estimate the unknown value of a population parameter

Statistical Inference: The procedures used to make conclusions about population parameters on the basis of sample statistics

Confidence Interval: an interval of values that the researcher is pretty sure will cover the true, unknown value of the population parameter

Hypothesis/Significance Testing: uses sample data to try to reject a hypothesis about the population. Usually to reject the notion that chance can explain the sample results, Null Value: a value that would indicate that nothing interesting is happening, Statistical Significance: rejecting the idea that the observed results are possible ir the null value is correct

Questions about Parameters

Parameter 1: population proportion

a number between 0 and 1 that represents the proportion with that certain trait

Parameter 2: Difference in two population proportions

this is used to compare some feature of the two populations

Parameter 3: Population Mean

the average variable for everyone in the population

Parameter 4: Population Mean for paired differences

the mean that would be obtained if we took the differences for the entire population of possible pairs, Paired differences: data formed by taking the differences in matched pairs

Parameter 5: Difference in Two Population Means

parameter of interest when comparing the mean for the same quantitative variable in two different populations or two population groups formed by a categorical variable

Sampling Distributions

Sampling Distribution: the probability distribution of a sample statistic, describing how values of a sample statistic vary across all possible random samples of a specific size that can be taken from a population

Standard Deviation of x: standard deviation of the sampling distribution of the sample mean

Standard Deviation of p: standard deviation of the sampling distribution of a sample proportion

Standard Error: estimated value of the standard deviation of a statistic