Microsoft Office Tutorials and References
In Depth Information
Table 6.16 Summary of test statistics used in inferential data analysis
Rule for Rejecting null
hypothesis
Test statistic
Application
x 2 –Testof
Independence
2 (calculated) >
2
Categorical Data
χ
= χ
α
(critical) or p-value <
= α
z Test
Two Sample Means of
Categorical and Interval Data
Combined
zstat>
=
z Critical Value
zstat<
– z Critical Value or
p-value <
=
= α
t Test
Two Samples of Unequal
Variance; Small Samples (<
30 observations)
tstat>
=
t Critical Value
tstat<
– t Critical Value or
p-value <
=
= α
ANOVA: Single Factor
Three or More Sample Means
F Stat > = F Critical Value or
p-value < = α
ANOVA: Two Factor
without Replication
Randomized Complete Block
Design
ANOVA: Two Factor
with Replication
Factorial Experimental Design
Key Terms
Sample
Sampling Error
Cause and Effect
Treatments
Response Variable
Paired t-Test
Nominal
Chi-square
Test of Independence
Contingency Table
Counts
Test of the Null Hypothesis
Alternative Hypothesis
Independent
Reject the Null Hypothesis
Dependent
χ
t-Test
t-Test: Two-Samples Unequal
Variances
Paired or Matched
t-Test: Paired Two-Sample For
Means
ANOVA
Main and Interaction Effects
Factors
Levels
Single Factor ANOVA
F-Statistic
Critical F-Value
Experimental Design
Observational Studies
Experiment
Completely Randomized Design
Experimental Units
Randomized Complete Block Design
Factorial Design
Replications
ANOVA: Two-Factor without
Replication
ANOVA: Two-Factor with
Replication
2 Statistic
2
χ
–level of significance
CHITEST(actual range, expected
range)
z-Test: Two Sample for Means
z-Statistic
z-Critical one-tail
z-Critical two-tail
P(Z<
,
α
α
=
z) one-tail and P(Z<
=
z
two tail
 
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