Assumptions for parametrical tests:
1. Normality – Data in each group should be normally distributed.
2. Equal Variance – Data in each group should have approximately equal variance.
3. Independence – Data in each group should be randomly and independently sampled
from the population.
4. No Outliers – There should be no extreme outliers.
Chi-square: Your data must meet the following requirements: Two categorical
variables. Two or more categories (groups) for each variable. Independence of
observations.
One sample t-test: used to determine whether an unknown population mean is different
from a specific value
Independent samples t-test: Used if you want to compare the means of exactly two
groups.
Mann Whitney U (Wilcoxon rank-sum): Mann Whitney U is the non-parametric alternative
test to the independent sample t-test. It is a non-parametric test that is used to compare two
sample means that come from the same population, and used to test whether two sample
means are equal or not.
Assumptions:
1. The sample drawn from the population is random.
2. Independence within the samples and mutual independence is assumed. That means
that an observation is in one group or the other (it cannot be in both).
3. Ordinal measurement scale is assumed.
Paired sample t-test (Dependent samples t-test): Paired sample t-test is used to compare
the means of two variables for a single group. The procedure computes the differences
between values of the two variables for each case and tests whether the average differs
from 0.
Wilcoxon signed-rank test: The non-parametric equivalent of the paired t-test.
Pearson’s correlation: parametric, analyses correspondence between actual data x&y
Spearman’s rho correlation: non parametric, first ranks data for x&y and analyses these
ranks.
Regression: used if you want to look at the relationship between 2 quantitative
variables→ leverages predictors to fit a lineair association model. (Y=a+bx+e)
a= intercept b=slope e=residual
Assumptions: Linearity, Normal residuals, Independent observations, homoscedasticity &
outliers
1. Normality – Data in each group should be normally distributed.
2. Equal Variance – Data in each group should have approximately equal variance.
3. Independence – Data in each group should be randomly and independently sampled
from the population.
4. No Outliers – There should be no extreme outliers.
Chi-square: Your data must meet the following requirements: Two categorical
variables. Two or more categories (groups) for each variable. Independence of
observations.
One sample t-test: used to determine whether an unknown population mean is different
from a specific value
Independent samples t-test: Used if you want to compare the means of exactly two
groups.
Mann Whitney U (Wilcoxon rank-sum): Mann Whitney U is the non-parametric alternative
test to the independent sample t-test. It is a non-parametric test that is used to compare two
sample means that come from the same population, and used to test whether two sample
means are equal or not.
Assumptions:
1. The sample drawn from the population is random.
2. Independence within the samples and mutual independence is assumed. That means
that an observation is in one group or the other (it cannot be in both).
3. Ordinal measurement scale is assumed.
Paired sample t-test (Dependent samples t-test): Paired sample t-test is used to compare
the means of two variables for a single group. The procedure computes the differences
between values of the two variables for each case and tests whether the average differs
from 0.
Wilcoxon signed-rank test: The non-parametric equivalent of the paired t-test.
Pearson’s correlation: parametric, analyses correspondence between actual data x&y
Spearman’s rho correlation: non parametric, first ranks data for x&y and analyses these
ranks.
Regression: used if you want to look at the relationship between 2 quantitative
variables→ leverages predictors to fit a lineair association model. (Y=a+bx+e)
a= intercept b=slope e=residual
Assumptions: Linearity, Normal residuals, Independent observations, homoscedasticity &
outliers
