Analysis with low sample size

Low sample size is a common phenomenon


Analysis with low sample size


Dealing with a low sample size? It increases the likelihood of a Type II error and decreases the power of the statistical analysis, thus affecting the results. If in doubt, we can perform a power calculation. Usually, the desired power is set to 0.8 or 0.9.

A common phenomenon

Low sample size is a common phenomenon in pilot-studies and experiments for hypothesis generation. It can cause an error where results reflect a single sample instead of the whole sample population. We can adjust the p-value, thus counteracting this effect and calculate bias, giving you the tool to adjust the interpretation of your result.



Buy low sample size analysis now

Bioinformatic analysis included


In the need for data analysis with bioinformatic annotations of the tendencies found? This feature can be added and you, of course, will get all features from our basic data analysis.

The analysis includes


  • Data filtration
  • Detection of unique identification
  • Data transformation and normalization test
  • Outlier estimation
  • Statistical testing
  • Bioinformatics enrichment analysis of
    • Cellular component
    • Molecular functions
    • Biological processes
    • Pathways
  • Bioinformatic investigation of potential crosslinking identifications in
    • Cellular component
    • Molecular functions
    • Biological processes
    • Pathways
  • Data visualization (Heatmap, PCA plot and Box-plots of regulations)
  • Bioinformatic visualization (Dot-plots)

  • Delivery time

    3 - 4 weeks. Fast delivery available.


    Technology used

    Machine learning is utilized to find outliers, to visualise data and bioinformatics, the latter is performed using the DAVID and Reactome database.

    Talk with our expert

    By requesting you agree to our privacy policy.