Statistical testing

Sign test:

- Non-parametric data (Nominal)

- Repeated measures or Matched pairs

- Test of difference

1. Add all pluses and minuses, S = whichever is lower (excluding 0 scoring pps)

2. N = total number of pps (excluding 0 scoring ones)

3. Find the tail test and level of significance and N to see the critical value

4. To be significant, the S must be equal to or less than the critical value

4. If it is higher, it is not significant, and we must accept the null hypothesis

Mann-Whitney test:

- Non-parametric data (Ordinal)

- Independent groups

- Test of difference

Wilcoxon test:

- Non-parametric data (Ordinal)

- Repeated measures or matched pairs

- Test of difference

Unrelated t-test:

- Interval data

- Repeated measures

- Test of difference

1. Find df (Na + Nb - 2)

2. Is it one or two tailed, what's the degree of significance?

3. That is the CV (calculated value), is it greater than or less than the S

4. Reject or accept the null hypothesis

Related t-test:

- Parametric data (interval or ratio)

- Repeated measures or matched pairs

- Test of difference

1. Find df (N-1)

2. Is it one or two tailed, what's the degree of significance?

3. That is the CV (calculated value), is it greater than or less than the S

4. Reject or accept the null hypothesis

Spearman rho test:

Pearson test:

- Parametric data (interval or ratio)

- Test of correlation

1. Find df (N-2)

2. Is it one or two tailed, what's the degree of significance?

3. That is the CV (calculated value), is it greater than or less than the S

4. Reject or accept the null hypothesis

Chi-squared test:

- Non-parametric data (Nominal)

- Independent groups

- Test of difference

1. Find df (no. of rows-1)x(no. of columns-1)

2. Is it one or two tailed, what's the degree of significance?

3. That is the CV (calculated value), is it greater than or less than the S

4. Reject or accept the null hypothesis