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.
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.
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.
3 - 4 weeks. Fast delivery available.
Machine learning is utilized to find outliers, to visualise data and bioinformatics, the latter is performed using the DAVID and Reactome database.