RESEARCH DISCIPLINE
Statistical Consulting for
Life & Biological Sciences
Statistical consulting for biological research serves laboratory, genomic, ecological, agricultural and veterinary researchers, from the experimental layout and sample size to the figures, unit counts and checklist items a journal will check.
Statistical consulting for biological research is specialist statistical support for studies of living systems, from cell and animal experiments and dose-response assays to sequencing studies, microbial communities and field surveys, with the design, analysis and reporting shaped by what the independent unit is, which measurements share an animal, a batch or a site, and how many outcomes or features are tested at once. Within Research Disciplines, these requirements also vary inside the family: the independent unit may be an animal, a donor, a culture, a pen or a field site, a project may test one prespecified endpoint or thousands of features, and the outcome may be a group mean, a fitted curve or a count with many zeros. OnlineSPSS is a specialist statistical consultancy for academic, clinical and professional research, and the analysis is carried out in-house by PhD-level statisticians.
RESEARCH FIELDS
Areas We Support Within Life & Biological Sciences
Ten fields share the replication and multiplicity problems of biological research but differ in what is measured, from wells and sequencing reads to animals in pens and species at field sites.
01
Biomedical Sciences
The preclinical stage of medical research, where disease models and candidate treatments are studied at the bench and in animals before any question is tested in patients.
Animal models
Cell culture
02
Genetics & Genomics
Questions of heredity: which variants run in families, differ between populations or accompany a disease, studied in human cohorts as well as in plant and animal breeding programs.
Association studies
Family studies
03
Bioinformatics
The statistical stage of omics research, which begins once sequencing and processing have produced expression, protein or metabolite tables and turns them into tested, validated findings.
Omics tables
Model validation
04
Biochemistry & Molecular Biology
Research at the level of molecules and pathways rather than whole organisms, from western blots and qPCR to enzyme kinetics and binding assays in purified systems or cell extracts.
Enzyme kinetics
Quantitative blots
05
Microbiology
Bacteria, fungi and viruses studied in pure culture, in antimicrobial susceptibility testing and in the mixed communities of soil, water, food and the gut.
Colony counts
Microbiome profiles
06
Toxicology
The harmful effects of chemicals, drugs and environmental agents, often studied under OECD test guidelines to support the exposure limits that regulators set.
Benchmark dose
Developmental studies
07
Immunology
The immune system in health, infection and vaccination, studied in human volunteers and animal models, from cell populations and antibody titers to infection challenge studies.
Flow cytometry
Cytokine panels
08
Ecology
Wild organisms, populations and communities in their own habitats, studied through field surveys, long-term monitoring and field experiments rather than in the laboratory.
Species abundance
Field surveys
09
Pharmacology
How compounds act on receptors, cells, tissues and whole animals, from potency at the bench to drug effects in vivo, separate from research on how patients use medicines.
In vitro assays
In vivo studies
10
Agricultural & Veterinary Sciences
Applied research on food production and animal health, carried out on research stations, commercial farms and veterinary practices, from crop varieties and feeds to vaccines for livestock and pets.
Field trials
Livestock studies
RESEARCH CONTEXT
Study Designs and Data Structures in Life & Biological Sciences
Several designs recur across the biological fields, each with its own unit of replication, and in experimental biology statistics the number of independent units, more than the number of measurements, sets what an experiment can show.
| Study design | Data structure | Where it recurs in this family |
|---|---|---|
| Cell and bench experiments | Wells in plates, plates in runs, runs on different days, technical repeats of one sample | Biochemistry & Molecular Biology and Biomedical Sciences, with cell assays in Immunology and Pharmacology |
| Animal studies | Animals housed in cages or pens, repeated body or tumor measures, litters, humane endpoints | Biomedical Sciences, Pharmacology and Toxicology, with livestock and companion animals in Agricultural & Veterinary Sciences |
| Dose-response and exposure designs | Several concentrations or doses per specimen, curves with few points, values below detection | Pharmacology and Toxicology, with stimulation series in Immunology |
| High-throughput omics | Thousands of features per sample, few samples, batches, normalization steps | Genetics & Genomics and Bioinformatics, with transcriptomics in Biomedical Sciences and Immunology |
| Genetic association and breeding populations | Genotypes at many loci, relatedness and ancestry structure, case-control or family samples | Genetics & Genomics first, then breeding programs in Agricultural & Veterinary Sciences |
| Microbial cultures and community profiles | Counts per plate, compositional read counts, samples repeated over time or sites | Microbiology, with microbiomes in Immunology and Agricultural & Veterinary Sciences |
| Field sampling and surveys | Counts or presence at sites and plots, repeated visits, spatial and seasonal dependence | Ecology, with wildlife and pasture sampling in Agricultural & Veterinary Sciences |
| Blocked, split-plot and herd trials | Treatments on plots, pens or herds, blocks, repeated measures over a season | Agricultural & Veterinary Sciences, with blocked field experiments in Ecology |
METHODOLOGICAL CONSIDERATIONS
Statistical Issues That Matter in Life & Biological Sciences
These issues recur across the family, and each bites hardest in a different field, from technical repeats at the bench to batches in sequencing runs and prespecified exclusions in small animal groups.
Technical Versus Biological Replicates Repeated wells, blots or reads of one sample measure the assay, not the biology. The unit differs by field: the animal or independent experiment at the bench, the pen in livestock work, the site in ecology.
Pseudoreplication and Clustering Animals share cages, calves share pens, plots share fields and samples share a day. Treating every row as independent inflates the apparent sample size, so the shared structure enters as blocks or random effects.
Batch and Run Effects Plates and days at the bench, staining runs and operators in flow cytometry, lanes and extraction kits in sequencing, and observers and seasons in field surveys all shift measurements. Each field records its own sources, and each source enters the design as a block and the model as a term.
Many Features and Many Endpoints Omics experiments test thousands of features, while dose studies test several doses and endpoints. Omics work controls the false discovery rate, and dose studies prespecify the primary contrasts.
Skewed, Bounded and Censored Outcomes Cytokine and residue concentrations are right-skewed with values below detection, survival and germination are proportions bounded at 0 and 1, and species and parasite counts carry excess zeros. The model follows how each value was recorded, including the values that a detection limit hid.
Small Groups and Prespecified Exclusions Groups of three to eight, or a few pens or plots where the group is the unit, limit what diagnostics can show. Randomization, blinding and exclusion rules are written into the plan before data collection.
ANALYTICAL NEEDS
Common Statistical Questions in Life & Biological Sciences
In life science statistical analysis, each recurring research need maps to a family of methods, and the choice within it follows the design, the unit of replication and the type of outcome.
Dose-Response and Kinetic Curves
Potency curves in pharmacology, enzyme kinetics in biochemistry and benchmark-dose modeling in toxicology all fit nonlinear models to a few doses or concentrations, with the parameter of interest reported with an interval.
Repeated Measures per Animal or Plot
Several readings from the same animal, plot or culture call for mixed-effects models that represent the correlation, with a random term for the cage, pen or block.
Few Units per Group
Groups of a handful of independent units call for effect sizes with intervals and, where randomization allows, exact or permutation tests, rather than a conclusion drawn from a p-value alone.
Profiles With Many Variables
Species composition, microbiome profiles and expression matrices hold more variables than samples, so ordination, clustering and dimension reduction treat the sample as the replicate.
Time to Death or Event
Survival to a humane endpoint or time to infection, with animals still event-free when the study ends, calls for survival methods that handle censoring.
Pooled Laboratory Evidence
Combining animal or ecological studies needs effect sizes on a common scale and a random-effects model for heterogeneity, and sometimes a decision not to pool.
REPORTING & RESEARCH STANDARDS
Reporting Standards in Life & Biological Sciences
Reporting expectations follow the type of experiment and assay more than the field, and they shape how replicates, exclusions and uncertainty are presented.
Animal studies report to ARRIVE 2.0, which asks for the experimental unit, the sample size justification, randomization, blinding and exclusions of each experiment. Randomized livestock trials can follow REFLECT and veterinary observational studies STROBE-Vet, and studies submitted to the British Journal of Pharmacology follow that journal’s guidance on experimental design and analysis. Quantitative PCR reports to MIQE 2.0, sequencing experiments describe their data to MINSEQE, human microbiome studies report to STORMS, environmental and animal-associated microbiome studies to STREAMS, and plant phenotyping datasets are described to MIAPPE. Systematic reviews of laboratory or animal evidence report to PRISMA 2020. Journals then add their own requirements, such as the Transparency and Openness Promotion Guidelines (TOP 2025) and the Nature Portfolio Reporting Summary. Estimates carry confidence intervals, the number of independent units is stated for each group, and exclusions and outlier rules are documented.
ANALYTICAL SUPPORT
Statistical Support for Biological and Laboratory Studies
Support covers the experiment before it starts, the analysis once the data exist and the review of work already done, within the scope agreed before work begins.
Research Planning & Study Design
Units, blocking and sample size set before the experiment
Quantitative Data Analysis
Analysis of assay, animal, omics and field survey data
Results Reporting & Interpretation
Effect estimates with intervals and clear unit counts
Independent Statistical Review
Independent review of replicate handling before submission
OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
SOFTWARE
Statistical Software Used in Life & Biological Sciences Research
Biological data analysis runs in the software that fits the data and the laboratory workflow, from curve fitting in Prism to omics workflows in R and Python, with scripts or syntax provided.
RESEARCHER VOICE
What Researchers Say
Feedback from two researchers, on replicates, detection limits and small groups analyzed from the experimental unit, and on repeated measurements built into the analysis from the start.
“We needed someone comfortable with biological data where technical replicates, skewed measures, detection limits, and small experimental groups can change the analysis completely. The team focused on the experimental unit and data-generating process first, which gave us much more confidence in the final results.”
“We contacted OnlineSPSS because our dataset was not a textbook example and we needed someone familiar with Agricultural & Veterinary Sciences. They focused immediately on the repeated or paired structure of the measurements, then built the analysis around the actual study design. The final output was concise and easy to use with the wider team.”
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Consulting for Life & Biological Sciences
Short answers to what laboratory, field and agricultural researchers ask before sharing a project, from studies that cross fields and reporting checklists to sample layout, confidentiality and the assessment process.
My study crosses two fields, such as microbiome sequencing in a mouse experiment. How is the analysis planned?
The analysis follows the design and the data, not the field label, so a study that crosses fields takes on the requirements of each. Mice that share a cage share their gut microbes, so cage effects are modeled and, when a treatment is given per cage, the cage is the unit; the read counts are compositional and need models built for that. A life science research statistician plans both parts together and records the unit, the model and the multiplicity rule in one analysis plan.
Does a study need more than one reporting checklist?
Often yes, because reporting guidelines attach to parts of a study rather than to a field. An animal experiment with a qPCR readout reports the experiment to ARRIVE 2.0 and the assay to MIQE 2.0, and a sequencing study describes its data to MINSEQE while the paper follows the journal’s own reporting summary. We align the statistical methods, unit counts and tables with each checklist your target journal names.
How should samples be laid out across plates, cages, sequencing runs or field sites?
Spread treatment groups across plates, days, sequencing runs and field blocks, so that no part of the layout coincides with a group. A bench assay balances groups within each plate and day, an animal study randomizes animals, or whole cages when the treatment is given per cage, a sequencing study distributes groups over runs and lanes, and a field experiment places treatments within blocks of similar habitat. We can design the layout and size the study on independent units before the first sample is processed. Our Statistical Methods pages show how the analyses are chosen.
How do you protect my research data and confidentiality?
Raw laboratory data, experimental protocols, sequencing metadata and analysis files are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Life Science or Biological Research With Us
Share your research question, experimental design, available data and analytical requirements. We review them against the replication, batch and multiplicity issues of your assay or field design first.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014