| Terms |
Definitions |
|
platkurtic
|
relatively flat
|
|
Mann-Whitney
|
t-test for independant samples
|
|
Nominal
|
Classify or categrize individuals. Each score does not actually indicate an amount; rather, it is used for identification. When you see nominal, think name.
|
|
Interval
|
Actual quantity. An equal amount separates any adjacent scores. For interval scores remember equal intervals between them.
|
|
probability committing Type II error even tho null hypothesis is true?
|
|
|
null hypothesis
|
the difference between two population means is null or zero .. there is no diffference! symbol =h0. Need it because you can ever really prove something true.
|
|
Median
|
The score that falls in the middle.....calculate by aligning scores from highest to lowest and pick score that falls in the middle
|
|
Validity
|
How well the test measures a construct
|
|
Peakedness/Kurtosis
|
Describes how peaked or flat the distribution is
|
|
Standard Error
|
The standard deviation of the distribution of sample means. σM
|
|
mutually exclusive
|
when its impossible for both outcomes to occur for a given individual
|
|
Variance
|
How much variation there is among n number of scores in a distribution, how much each score differs from the mean
|
|
Ratio variables
|
Have order, equal intervals, and a real zero (Ex: age)
|
|
power
|
is the probability that the test will reject the hypothesis tested when a specific alternative hypothesis is true. To calculate it of a given test it is necessary to specify α (alpha, i.e., the probability that the test will lead to the rejection of the hypothesis tested when that hypothesis is true) and to specify a specific alternative hypothesis.
|
|
committing Type II error even tho null hypothesis is FALSE
|
Beta
|
|
Inferential stats
|
Use samples to infer something about populations
|
|
type II error
|
failing to reject the null hypothesis when it is false
|
|
Pearson r correlation coefficient
|
ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation)
|
|
Negative correlation
|
As one variable goes up, the other goes down
|
|
descriptive statistics
|
you are simply describing what is or what the data shows
|
|
one tail (right)
|
prior evidence u>. aH0: u lessthen/= aH1: mu greatr than a
|
|
degrees of freedom:
|
# of pieces of information that are free of eachother aka they cannot be deduced from one another. as degree of freedom increases so does accuracy.
|
|
sampling error formula
|
x - u (sample mean minus population mean)
|
|
sampling distribution of the mean
|
repeatedly sample from a population and form a distribution of sample means
|
|
characteristics explained by the central limit theorem
|
1. the mean of the sampling distribution of the means will always equal the population mean.2. standard error of the mean represents an average deviation of the sample means from the population means. 3.standard error of the mean gets smaller as the sample size increases and the variability of the scores in the population decreases4. the sampling dist of the mean takes the shape of a normal distribution as the sample size gets larger.
|
|
why are power calculations used?
|
used to find the probability that your experiment will detect an effect (or difference) in the world, if it exists
|
|
the larger the standard error:
|
the more variability there is in the set of sample means
|
|
type II error?-occurs with what probability
|
When you accept/fail to reject your null, and in reality it is false-occurs with probability of beta
|
|
the critical value is effected by
|
alpha and the number of tails. If alpha=.05 and we are using a two-tailed test, then the critical value is 1.96 (from the Z tables).
|
|
How do we show a statement is most likely false? (5 steps)
|
1. Create a statement (null hypothesis)2. Create a distribution of outcomes given that hypothesis 3. Determine outcomes we consider unlikely4. Evaluate if our experimental result is one of those unlikely outcomes5. two possible outcomes
|
