2ND SEM MIDTERMS (PSYCH STATS) Flashcards
tells whether the score is located above (+) or below (-) the mean
sign
tells the distance between the score and the mean in terms of the number of standard deviations.
standardize entire distribution
number
specifies the precise location of each X value within a distribution. The numerical value of it specifies the distance from the mean by counting the number of standard deviations between X and m.
provide an objective method for determining how well a specific score represents its population
z-score
composed of scores that have been transformed to create predetermined values for m and s. Are used to make dissimilar distributions comparable.
standardized distribution
PROPERTIES OF DISTRIBUTION OF Z-SCORES
- Shape- The distribution of z-scores will have exactly the same shape as the
original distribution of scores - Mean- The z-score distribution will always have a mean of zero
- Standard Deviation- The distribution of z-scores will always have a standard deviation of s = 1
the____ for any specific outcome is defined as a fraction or a proportion of all the possible outcomes.
probability
requires that each individual in the population has an equal chance of being selected
Random sampling
sample obtained: simple random sample
requires that each individual has an equal chance of being selected and that the probability of being selected stays constant from one selection to the next if more than one individual is selected
Independent random sampling
sample obtained: independent random sample
the percentage of individuals with scores at or below a particular X value
Percentile rank
the collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a population.
distribution of sample means
a distribution of statistics obtained by selecting all the possible samples of a specific size from a population
sampling distribution
For any population with mean m and standard deviation s, the distribution of sample means for sample size n will have a mean of m and a standard deviation of s/√n and will approach a normal distribution as n approaches infinity.
Central limit theorem
The mean of the distribution of sample means is equal to the mean of the population of scores, m, and is called the
expected value of M
The standard deviation of the distribution of sample means, sM. It provides a measure of how much distance is expected on average between a sample mean (M) and the population mean (m).
standard error of M
states that the larger the sample size (n), the more probable it is that the sample mean will be close to the population mean.
law of large numbers
a statistical method that uses sample data to evaluate a hypothesis about a population.
Hypothesis testing
states that in the general population there is no change, no difference, or no relationship. In the context of an experiment, it predicts that the independent variable (treatment) has no effect on the dependent variable (scores) for the population
null hypothesis (H0)
states that there is a change, a difference, or a relationship for the general population. In the context of an experiment, it predicts that the independent variable (treatment) does have an effect on the dependent variable.
alternative hypothesis (H1)
or the level of significance, is a probability value that is used to define the concept of “very unlikely” in a hypothesis test.
alpha level
composed of the extreme sample values that are very unlikely (as defined by the alpha level) to be obtained if the null hypothesis is true. The boundaries for it are determined by the alpha level. If sample data fall in the it, the null hypothesis is rejected.
critical region
error occurs when a researcher rejects a null hypothesis that is actually true. In a typical research situation, a Type I error means the researcher concludes that there is evidence for a treatment effect when in fact the treatment has no effect.
Type I
occurs when a researcher fails to reject a null hypothesis that is in fact false. In a typical research situation, it means that the hypothesis test has failed to detect a real treatment effect.
Type II error
A result that is very unlikely to occur when the null hypothesis is true. That is, the result is sufficient to reject the null hypothesis. Thus, a treatment has a significant effect if the decision from the
hypothesis test is to reject H0.
significant or statistically significant
the** statistical hypotheses** (H0 and H1) specify either an increase or a decrease in the population mean. That is, they make a statement about the direction of the effect
directional hypothesis test, or one-tailed test
intended to provide a measurement of the absolute magnitude of a treatment effect, independent of the size of the sample(s) being used.
effect size
statistical test of it is the probability that the test will correctly reject a false null hypothesis. That is, the probability that the test will identify a treatment effect if one really exists
power
It is computed from the sample variance or sample standard deviation and provides an estimate of the standard distance between a sample mean M and the population mean m.
estimated standard error (sM)
used to test hypotheses about an unknown population mean, m, when the value of s is unknown. The formula for the it has the same structure as the z-score formula, except that it uses the estimated standard error in the denominator
t statistic
describe 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 of it for a sample with n scores
Degrees of freedom
the complete set of t values computed for every possible random sample for a specific sample size (n) or a specific degrees of freedom (df). It approximates the shape of a normal distribution
t distribution
an interval, or range of values centered around a sample statistic. The logic behind it is that a sample statistic, such as a sample mean, should be relatively near to the corresponding population parameter. Therefore, we can confidently estimate that the value of the parameter should be located in the interval near to the statistic.
confidence interval
A research design that uses a separate group of participants for each treatment condition (or for each population) is called an
independent-measures research design or between-subjects design
research design that uses the same group of individuals in all of the different treatment conditions is called a
repeated-measures design or within-subjects design.
each individual in one sample is matched with an individual in the other sample. The matching is done so that the two individuals are equivalent (or nearly equivalent) with respect to a specific variable (or variables) that the researcher would like to control.
matched-subjects design
