Any phenomenon that affects some, but not all, samples (or all samples if they are being compared to an external dataset).

• the person who ran the samples
• the data analyst / processing pipeline used, if different across samples
• the technology or platform used
• contamination of a sample
• whether it was very hot outside that day
• if some of the animals came from the same litter
• unmeasured or unknown similarities between samples (e.g., 5 samples all from very old people, other 5 are a mix).
• etc etc etc

For further reading, please consider this review article from Nature Genetics, titled: Tackling the widespread and critical impact of batch effects in high-throughput data.