Statistics and Probability

• Finite Population - Type of population that consists of finite or fixed number of elements, measurements, or observations
• Infinite population - Type of population that contains, hypothetically at least, endless elements
• Estimate - is the value or range of values that approximates the population value.
• Point estimate - is a specific numerical value of a population parameter
• Sample mean - It is the best estimate of a population mean.
• Interval estimate - Also called a confidence interval
• Confidence level - is the probability that the interval estimate will contain the trues population parameter
• Critical values - are the z-values that are used in describing the characteristics of a target population
• Critical values - also known as confidence coefficients
• Margin of error - is the maximum difference between the observed sample mean and true value of the population mean
• Degrees of freedom - are the numbers of values that are free to vary after a sample statistics has been computed
• The t-distribution is a probability that is ised to estimate population parameters when the sample is 1. Small and/or when the 2. Standard deviation is unknown
• William Sealey Gosset - developed the t-distribution in 1908
• Like the normal distribution, the t-distribution is 1. Bell shaped, symmetrical about 2. 0 and has the total area under its curve equal to 3. 1
• The t-distribution has tails that are asymptotic to the 1. Horizontal/x axis
• The mean, median and, mode of the t-distribution are all equal to 0
• The shape of the t-distribution curve depends on the number of degrees of freedom
• The t-distribution has lower peak and heavier/thicker tails than the normal curve
• As the degree of freedon increases, the t-distribution looks more and more like the normal distribution
• The variance and standard deviation of the t-distribution is always greater than 1
• Percentile - term that describes how a score compares to other scores from the same set
• T-table - a critical tool used extensively in hypothesis testing
• If it less than 30 - when can we say that sample size is small
• Central Limit Theorem - it states that if a sample size n where n is sufficiently large is drawn from any population with a mean and standard deviation then the sampling distribution of sample means approximates a normal distribution