Unit 2 Test Flashcards
Raw Score
unchanged scores that are the direct result of measurement
Z-Score
specifies the precise location of each X value within a distribution
Deviation Score
measures the distance in points between X and μ and indicates whether X is located above or below the mean
Z-Score Transformation
if every X value is transformed into a z-score then the distribution of z-scores will have the following properties: Normal Shape, mean of 0, standard deviation of 1
Standardized Distribution
composed of scores that have been transformed to create predetermined values for μ and σ
Standardized Score
scores that have been standardized
Probability
fraction or proportion of all the possible outcomes
Random Sample
requires that each individual in the population has an equal chance of being selected
Independent Random Sample
probability of selecting any particular individual is independent of those individuals who have already been selected for the sample
Sampling with Replacement
Sampling with Replacement to keep the probabilities from changing form one selection to the next, it is necessary to return each individual to the population before you make the next selection
Unit Normal Table
complete listing of z-scores and proportions
Percentile Rank
the percentage of the individuals in the distribution who have scores that are less than or equal to the specific score
Percentile
when a score is referred to by it’s percentile rank
sampling error
the natural discrepancy, or amount of error, between a sample statistic and its corresponding population parameter
distribution of sample means
collection of sample means for all of the possible random samples of a particular size (n) that can be obtained from a population
Sampling Distribution
distribution of statistics obtained by selecting all of the possible samples of a specific size from a population
Central Limit Theorem
for any population with a mean μ and standard deviation σ, the distribution of sample means for sample size n will have a mean of μ and a standard deviation of σ/ √n and will approach a normal distribution as n approaches infinity
Expected Value of M
the mean of the distribution of sample means is equal to the mean of the population of scores, μ
Standard Error of M
standard deviation for the distribution of sample means. provides a measure of how much distance is expected on average between a sample mean (M) and a population mean (μ)
Law of large numbers
the larger the sample size (n), the more probable it is that the sample mean is close to the population mean
Hypothesis Test
statistical method that uses sample data to evaluate a hypothesis about a population
Null Hypothesis
in the general population there is no change, no difference, or no relationship. Independent variable has no effect on the dependent variable for the population
alternative hypothesis
there is a change, difference, or relationship for the general population. independent variable has an effect of the dependent variable
level of significance
is a probability value that is used to define the concept of “very unlikely” in a hypothesis test
critical region
extreme sample values that are very unlikely to be obtained if the null hypothesis is true.
Type I error
(say treatment has an effect when there is no effect)
Type II Error
say treatment has no effect when there is an effect
Beta
probability of a type II error
Significant
when a result is very unlikely to occur when the null hypothesis is true
Test Statistic
indicates that the sample data are converted into a single, specific statistic that is used to test a hypotheses
Directional Test/ One-Tailed Test
statistical hypotheses specify either an increase or a decrease in the population mean
Effect Size
provide a measurement of the absolute magnitude of a treatment effect
Cohen’s d
measures mean difference in terms of the standard deviation
Power
probability that the test will correctly reject a false null hypothesis
Alpha Level
probability that the test will lead to a Type I error if the null hypothesis is true
estimated standard error
an estimate of the real standard error when the value of σ is unknown
t statistic
used to test hypotheses about an unknown population mean μ, when the vaule of σ is unknown
degrees of freedom
describe the number of scores in a sample that are independent and free to vary
t distribution
complete set of t values computed for every possible random sample for a specific sample size (n) or a specific degrees of freedom (df)
estimated d
measure of effect size when population values are not known and you must substitute the corresponding sample values in their place
r^2
percentage of variance accounted for by the treatment
confidence interval
interval, or range of values, centered around a sample statistic
