In biological and clinical research, comparing wild-type versus mutant samples or healthy versus diseased individuals is essential. It may be analyzing gene expression, blood counts, or methylation levels, note that the variability is inevitable, making reliable and interpretable statistical approaches necessary.
Null Hypothesis (H₀): Assumes no difference between groups.
Alternative Hypothesis (H₁): Suggests a difference exists.
Calculate a statistic (e.g., difference in means) and compare it to a reference (null distribution) to determine significance (P-value).
An intuitive approach involves randomizing sample labels to generate a null distribution, then comparing the observed difference.
set.seed(100)
geneA = rnorm(70, mean = 8, sd = 6)
geneB = rnorm(70, mean = 7, sd = 7)
org.diff = mean(geneA) - mean(geneB)
exp.null = replicate(1000, mean(sample(geneA, 70)) - mean(sample(geneB, 70)))
p.val = mean(exp.null >= org.diff)
Combine both groups into a single pool.
Shuffle and split repeatedly to calculate new differences in means.
Compare the observed difference to this null distribution for a P-value.
When data is scarce or time is limited, the t-test is a faster and reliable method
# Welch’s t-test with unequal variance
t.test(geneA, geneB)
Parametric CI assumes data follows a known distribution and uses formulas to calculate the interval.
Bootstrap CI resamples data repeatedly to empirically estimate the interval, providing flexibility when assumptions of normality are questionable.
Running many tests increases false positives. Techniques like the Bonferroni Correction or Benjamini-Hochberg control for this by adjusting P-values, ensuring robust conclusions.
The power of your test improves with larger sample sizes and greater effect sizes.
Let us continue to harness statistics for maximum impact in research!
#Bioinformatics #DataScience #StatisticalAnalysis #HypothesisTesting #ClinicalResearch #ConfidenceIntervals #RandomizationTest #Python #RStats