Parametric Tests and NonParametric Tests for Human Resources
16/02/2024Statistical Tests play a crucial role in analyzing data related to various HR functions like recruitment, employee satisfaction, performance evaluations, and training outcomes. These analyses often involve making comparisons or understanding relationships within the data. Statistical tests are broadly categorized into parametric and nonparametric tests, each suitable for different types of data and assumptions.
Parametric Tests
Parametric tests are statistical analysis techniques that assume the data follows a certain distribution, typically a normal distribution. They are used when the data meets specific criteria, including interval or ratio scale, normal distribution, and homogeneity of variance. Parametric tests are powerful and provide more precise outcomes when their assumptions are met.
Common Parametric Tests in HR:

ttest:
Used to compare the means of two groups (e.g., comparing the average performance scores of two departments).

ANOVA (Analysis of Variance):
Allows comparison of means among three or more groups (e.g., evaluating job satisfaction across different job levels).

Linear Regression:
Assesses the relationship between two continuous variables (e.g., the relationship between training hours and job performance).

Pearson Correlation:
Measures the strength and direction of the relationship between two continuous variables (e.g., the correlation between employee satisfaction and retention rates).
NonParametric Tests
Nonparametric tests, also known as distributionfree tests, do not assume your data follows a specific distribution. These tests are more flexible and can be used with ordinal data or when the assumptions for parametric tests are not met, such as when data does not follow a normal distribution or when sample sizes are small.
Common NonParametric Tests in HR:

MannWhitney U Test:
Comparable to the ttest but for two independent samples where assumptions of normality are not met. It can be used to compare satisfaction levels between two teams.

Wilcoxon SignedRank Test:
Used for comparing two related samples or repeated measurements on a single sample to assess differences in median (e.g., before and after analysis of a training program on employee skills).

KruskalWallis H Test:
An alternative to ANOVA for comparing more than two groups when the data does not meet parametric assumptions (e.g., comparing engagement levels across multiple departments).

Spearman’s Rank Correlation:
Measures the strength and direction of association between two ranked variables (e.g., ranking of employees by performance and by satisfaction).
Choosing Between Parametric and NonParametric Tests
The choice between parametric and nonparametric tests in HR research depends on several factors:

Data Level:
Parametric tests are typically used for interval or ratio data, while nonparametric tests are suitable for ordinal or nominal data.

Distribution Assumption:
If the data follows a normal distribution and other assumptions (e.g., homogeneity of variances) are met, parametric tests are preferred for their statistical power. If these assumptions are violated, nonparametric tests are more appropriate.

Sample Size:
Parametric tests generally require larger sample sizes. Nonparametric tests can be more suitable for smaller samples.
Application in HR
Understanding and choosing the appropriate statistical test is crucial in HR analytics for making informed decisions. For instance, when evaluating the effectiveness of a new training program, an HR analyst might use a ttest or MannWhitney U Test depending on the data distribution. Similarly, understanding employee engagement across different departments might involve ANOVA or KruskalWallis tests based on the data’s nature.
Parametric Tests  NonParametric Tests  
1  Assume normal distribution  No distribution assumption 
2  Interval/ratio data needed  Nominal/ordinal data acceptable 
3  More statistical power  Less statistical power 
4  Sensitive to outliers  Less sensitive to outliers 
5  Larger sample sizes preferred  Suitable for small samples 
6  Homogeneity of variance required  No variance homogeneity requirement 
7  Linear relationships  Any relationship type 
8  Examples: ttest, ANOVA  Examples: MannWhitney, KruskalWallis 
9  Assumes equal variances  Does not assume equal variances 
10  Parametric confidence intervals  Nonparametric confidence intervals 
11  Requires precise measurements  Can work with ranks or scores 
12  More assumptions to check  Fewer assumptions to check 
13  Can predict outcomes  Describes data 
14  Often involves estimation of parameters  Often involves median or mode 
15  Generally faster computation  Computation may be more complex 