Steps of hypothesis testing
• Step 1: The null hypothesis is states and a second statement called
the alternative hypothesis is made. The alternative Hypothesis is
accepted if the null hypothesis is rejected.
• Step2: A level of significance is chosen. Usually 0.05 and 0.01 level is
selected. It is refers to the probability of making a type I error.
• Step 3: A test statistic is chosen. This value will be
used in step 4 and step 5 to arrive at a decision
regarding the null hypothesis.
• Step 4: A decision rule is set up based on the level of
significance chosen in step 2 and the sampling
distribution of the test statistic from step 3.
• Step 5: Finally, the decision rule in step 4 is used to
make a decision- either to reject the null hypothesis
or not to reject the null hypothesis.
Analysis
• Univariate (One variable at a time)
• Bivariate (Two variable at a time)
• Multivariate (More than two variable at a time)
Variable
• Categorical
• Possible Bivariate Analysis:
• Categorical Vs Categorical
• Categorical Vs Numerical
• Numerical Vs Numerical
Numerical
1. Categorical vs Categorical
X>2, Y>2
X=2, Y=2
Unrelated
Related
Unrelated
- Pearson’s Chi square
- Mc. Nemar
-Pearson’s Chi square
- Yates's Chi square
- Binomial
-Linear by Linear
- Fishers Exact Chi square
X :Group variable
Y :Outcome variable
-Likely-hood Ratio
2. Categorical vs Numerical
Parametric
X=2 &
Y: Normal
Unrelated
Related
Student’s t test <=30 `Paired ‘t’
Or
test
Z test N>30
X> 2 &
Unrelated
Y: Normal
Related
Non-Parametric
X=2 &
Y: Non Normal
Unrelated
Related
Wilcoxson rank-sum
Wilcoxson sign-rank
Or Mann-Whitney U
X>2
&
Unrelated
One way ANOVA Repeated measures ANOVA Kruskal Wallis
H test
Y: Non-Normal
Related
Freidmans test
3. Quantitative vs Quantitative
Number Vs Number
X: Normal and Y: Normal (Both should be normal)
- Pearson’s correlation coefficient (r)
X: Non-Normal and Y: Non-Normal or any one non –Normal
or Rank data
- Spearman’s rank correlation (ρ) Rho