General Terminology/Rules

Standard Error

the standard deviation of the sampling distribution (denominator of z/t-test) 

Sampling distribution

probability distribution of a statistic that is obtained through repeated sampling of a specific population

Central limit theorem

If the population is skewed, the sample mean distribution is approximately normal if n ≥ 30

Unbiased Estimator 

average equals the true population value it's trying to estimate. 

Consistant Estimator 

gets closer to the true value as sample size increases 

CLT Rules: 

Either the sample is taken from a normal population, or sample size is greater than or equal to 30

When to use: Finite population correction factor 

Sampling without replacement, or the sample size is more than 5% of the population  

Normal Approximation Rule 

population is approximately normal if 𝑛𝑝>=5 𝑎𝑛𝑑 𝑛(1−𝑝)>=5

When to use: Continuity correction factor  

use when you approximate a binomial distribution with a normal distribution

Continuity Correction Factor 

You add or subtract 0.5 to the discrete x-value when using the normal distribution

Point estimator 

estimates the value of an unknown parameter using a single value 

Interval Estimator 

draws inferences about a population parameter using an interval 

Type I Error (α) 

rejecting the null hypothesis when it is true 

Type II Error (β) 

not rejecting the null hypothesis when it is false 

P-Value: reject when

Reject if less than the significance level 

Operating Characteristic (OC) Curve: 

A graph that shows the probability of not rejecting the null hypothesis for different possible values of the true population parameter. 

Rejection Region: two tailed test 

Absolute value of test statistic is greater than the z/t score of given significance level

Observational data 

Data collected without controlling or influencing the subjects, just observing 

Experimental data

Data collected by applying treatments or making changes and measuring the results. 

Independent samples 

Two samples are independent if the observations in one sample don’t influence or relate to the observations in the other. 

Matched Pairs 

Two related values from the same or similar subjects, we look at the difference between them.