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Stats Vocabulary

Skewed distribution A distribution that is not symmetrical, but instead has a tail that trails to the right or the left; indicates that the mean, median, and more are not all the same number, but that the majority of the data is not gathered at the mean.
Interval scale A scale in which the values are evenly distributed and arranged in order.
Mean The average value of all of the data; unreliable because it is easily altered by an outlier.
Experimental method Involves controlled variables and observed variables, the manipulation of a variable, and the ultimate conclusion of causation.
Dependent variable The variable that is not manipulated, but that depends on the manipulated variable. This is usually what we are trying to measure.
Range Represents the amount of variability in the data, shown by subtracting the smallest value from the largest value.
Inferential statistics techniques that allow us to study samples and then make generalizations about the populations from which they were selected
Positively skewed when the tail of a distribution points towards the positive end on the x-axis (right-hand side)
Median the score that divides a distribution in half so that 50% of the individuals in a distribution have scores at or below the median
Population the set of all the individuals of interest in a particular study
Point-biserial correlation a special version of the Pearson correlation used to measure the relationship between two variables in situations where one variable is regular, numerical scores but the second only has two values
Interquartile range a range that ignores extreme scores and instead focuses on the range covered by the middle 50% of the distribution
Degrees of freedom the number of scores in a sample that are independent and free to vary; because the sample mean places a restriction on the value of one score in the sample, there are n-1 degrees of freedom for the sample
Descriptive statistics statistical procedures used to summarize, organize, and simplify data
Nonparametric test tests for data that are not arranged in numerical means, but by nominal or ordinal scales instead
Residual variance/error variance the variance that exists in a set of sample data; indicates that the sample variance represents unexplained and uncontrolled differences between scores
Normal distribution a distribution of scores that is symmetrical, in which the mean, median and mode are all the same value
Tail the ends of the distribution of scores that represent the least-occurring scores
Percentile rank a number which represents the place of a single score in relation to the rest of the scores
Central limit theorem For any population with mean (mew) and standard deviation (ro), the distribution of sample means for sample size n will have a mean of (mew) and a standard deviation of (ro)/square root n and will approach a normal distribution as n approaches infinity
Correlation used to measure and describe a relationship between two variables -- without manipulation or control of the variables, and without attempt to justify the relationship
Observed frequency the values that result from counting the number of n individuals in a category (chi-square test)
Power the probability that a test will correctly reject a false null hypothesis -- the probability that the test will identify a treatment effect if one really exists
Point estimate an estimation that uses a single number as an estimate of an unknown quantity; very precise
Main effect the difference between the means in ANOVA
Interaction between two factors -- occurs whenever the mean differences between individual treatment conditions, or cells, are different from what would be predicted from the overall main effects of the factors
Parametric test tests that concern parameters and require assumptions about parameters (t-tests, ANOVA, etc.)
Sample a set of individuals selected from a population, usually intended to represent the population in a research study
Sampling error the discrepancy, or amount of error, that exists between a sample statistic and the corresponding population parameter
Correlational method two different variables are observed to determine whether there is a relationship between them
Independent variable the variable that is manipulated by the researcher
Nominal scale A scale created purely by non-numerical information; categorical scale, like types of cars or authors.
Ratio scale A scale of evenly distributed values, but in which the zero value actually means zero.
Symmetrical distribution a distribution in which it is possible to draw a vertical line through the middle so that one side of the distribution is a mirror image of the other
Negatively skewed in a distribution; when the tail points to the left
Mode the value with the most frequency
Variability the average squared distance from the mean
Ordinal scale A scale arranged by ranks, like the first five runners in a race, in which the order matters.
Standard deviation the square root of the variance
Z scores the precise location of each X value within a distribution; the sign signifies whether the score is above the mean or below the mean; the number specifies the distance from the mean by counting the number of standard deviations
Critical region composed of extreme sample values that are very unlikely to be obtained if the null hypothesis is true; boundaries are determined by the alpha level; null hypothesis is rejected of the data fall in the critical region
Interval estimate in which a range of values is used as an estimate of an unknown quantity
Random sample requires that each individual in the population has an equal chance of being selected; probabilities must stay constant from one selection to the next
Body the larger section of the distribution
Binomial distribution shows the probability associated with each value of X from X=0 to X=n
Hypothesis test a statistical method that uses sample data to evaluate a hypothesis about a population
Alternative hypothesis states that there is a change, a difference, or a relationship for the general population; predicts that the independent variable does have an effect on the dependent variable
Type I error occurs when a researcher rejects a null hypothesis that is actually true; means that the researcher concludes that a treatment does have an effect when in fact it does not
Post-hoc tests additional hypothesis tests that are done after an ANOVA to determine exactly which mean differences are significant and which are not
Null hypothesis states that in the general population there is no change, no difference, or no relationship; predicts that the independent variable has no effect on the dependent variable
Type II error occurs when a researcher fails to reject a null hypothesis that is really false; means that the hypothesis test has failed to detect a real treatment effect
Created by: 654230637