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Statistics Terms
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Key Concepts:
Terms in this set (35)
central tendency
to determine a single value best representing the entire distribution
mean
the mathematical average, essentially
median
the middle value
mode
the most frequently occurring value within a set of larger values
variability
measures how well an individual value or group of values represents the entire distribution
range, standard deviation, variance
three commonly used measures of variability
range
the distance from the lowest to the highest value in the collected data set
standard deviation
standardizes the variance in standard deviation units; indicates how broadly the scores vary from each other
variance
how much the values vary within a collected data set
measures of central tendency
mean, median, mode
categorical variable
for example, gender
continuous variable
for example, age
descriptive statistics
organize and summarize data
inferential statistics
analyze samples make inferences and predictions about populations
independent samples t-test
a comparison of average scores or means from two separate sample groups, typically comparing means between a treatment and a control group;
statistical technique used to analyze the comparison of means from two independent or different sample groups to determine if they are statistically different
dependent samples t-test
a comparison of means from the same sample group, typically comparing means between some pre-test and post-test; a statistical technique used to analyze the comparison of means from two sample groups related in some way to determine if they are statistically different
ANOVA
a comparison of means from multiple sample groups
statistical significance
implies some form of an effect, a difference, or relationship exists between variables, with the likelihood of it not being due to chance, but to some other real influence; the difference between two measurements that results from more than randomness
hypothesis testing
used to determine statistical significance
null hypothesis (H0)
a "straw man"; the theory the researcher is attempting to falsify by experimentation
alternative hypothesis (Ha or H1)
what the researcher believes to be the true state of affairs
one-sided alternative hypothesis
gives the direction in which the true value differs from the hypothesized value; can be right-tailed or left-tailed
right-tailed
the true value of the population parameter being considered is greater than the value hypothesized in H0
left-tailed
the true value of the population parameter being considered is less than the value hypothesized in H0
two-sided alternative hypothesis
does not indicate direction
p value
what a hypothesis test returns; the probability of observing data as extreme or more extreme than what was observed given that the null hypothesis is true; a measure of how likely or unlikely it is for the collected data to have occurred given that the null hypothesis is true
a smaller p value
means that if the null hypothesis describes the true state of things that a rare event has occurred
a larger p value
means that the data collected are more in line with what is stated in the null hypothesis
reject null hypothesis
if the observed test statistic exceeds the critical value in the appropriate direction (depending on the direction of the alternative hypothesis)
Type I error
made when the researcher rejects the null hypothesis when the null hypothesis
is, in fact, true; occurs when the p value resulting from the hypothesis test is less than α
Type II error
occurs when the researcher does not reject the null hypothesis when, in fact, the null hypothesis is false; the probability of making an error of Type II is referred to as β and relates to the sensitivity, or power, of a significance test
β
the probability of making a Type II error; relates to the sensitivity (power) of a significance test
power
the sensitivity of a significance test; calculated as 1-β; the probability of correctly rejecting the null hypothesis under a specific set of circumstances
effect size
the size of difference that the researcher would consider meaningful
sample size
increased sample size increases sensitivity; a function of all the others--effect size, α, β, and the hypotheses
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