1a statement that something is true
hypothesis
2statement about the absence of any effect claimed for a certain action and assert equality, no signi. diff. no sig. rel. between variables
null hypothesis
3assertion contradicting the null hypothesis which the research wants to prove
alternative hypothesis
4probability of commiting a type 1 or alpha error in a test
level of significance
5is the formal procedures consisting of pertinent
steps whose major objective is to make decision
whether to accept or reject the hypothesis
based on sample evidence.
hypothesis testing
6an error that occurs when a true null hypothesis is rejected and
therefore the alternative hypothesis is false.s a range of values and when the test statistic falls within the
region of acceptance, the null hypothesis is not rejected.
type 1 error
7an error that occurs when a false null hypothesis is not rejected
and therefore alternative hypothesis is true.
type 2 error
8the set of values outside the region of acceptance and if the test
statistic falls within the rejection region, the null hypothesis is
rejected
rejection region
9is a range of values and when the test statistic falls within the
region of acceptance, the null hypothesis is not rejected.
acceptance region
10a value or values of the test statistic that separates the region of
rejection from the remaining values
critical value
11
are statisticaltests/tool for
population parameters such as
means, variances, and proportions
that involve assumptions about the
populations from which the samples
were selected.
Parametric tests
12a test of hypothesis where the region of
rejection is only one side of the distribution. The region of rejection would consist of a
range of number
one tailed (directional) test
13 a test of hypothesis where the region of
rejection is on both side of the distribution.
two-tailed (non-directional) test
14uses the data obtained from a sample to make a decision about whether the null hypothesis should be rejected
statistical test
15numerical value obtained from a statistical test
test value
16seven step in hypothesis testing
Step 1:
Formulate the problem
Step 2:
Formulate the Null and Alternative Hypothesis
Step 3:
Select the level of Significance
Step 4:
Choose the appropriate test statistic
Step 5:
State the critical value or point
Step 6:
Calculate the test statistic
Step 7:
Interpret
17a test involving the population mean and sample mean, and is used for large
sample size n>=30
z test
18If random samples of size (N) are drawn from a
large population with mean and finite nonzero variance
, then the
distribution of the sample mean approaches the normal probability
distribution.
central limit theorem
19a special table used to summarize and facilitate in stating the
critical value or point on hypothesis testing.
z table
20they compare
between two means to suggest whether both samples came from the same population.
t distribution and z distribution
21It is used when the samples are dependent, when there is only one sample that
has been tested twice or when there are two samples that matched or paired. see
dependent t test
22used to test whether population means are
significantly not related from each other, using the means from randomly selected
samples
two independent sample t test
23nonnegative, it starts out at zero and rises to a peak and
then falls again to zero as F increases without limit.
f distribution
24used for testing the equality of three or more population means
by analyzing sample variances using the F-distribution.
ANOVA single factor
25conditions assumed in analysis of variance of f test ANOVA single factor
Normally distributed, homogeneity of variance, independence
26used when the result is significant
Multiple Comparisons (Post-Hoc Test
27used when all pairs of means
will be compared at the same time and when do not have an idea which comparison to
make.
scheffe's method
28generates a report of univariate statistics, providing
information about the central tendency and variability of your data.
descriptive statistics
29probability of getting a sample statistic (mean) or a more extreme sample statistic in the direction of the alternative hypothesis when the null hypothesis is true
p-value
30useful when data can be classified along
two different dimensions.
two-way anova with replication
31Statistics under descriptives
sample size, mean, minimum, maximum,
standard deviation, variance, range, sum, standard error of the mean, and kurtosis and
skewness with their standard errors.