Fixed payload
Every duration must represent the same byte count and transfer boundary.
Unit Converters
Data Transfer Precision Calculator
Convert each repeated duration into an observation-level throughput, then calculate sample SD, RSD, range, and Type A uncertainty.
Transfer repeatability
Measure fixed-payload throughput precision run by run
Analyze repeated end-to-end durations for one unchanged payload. Each duration becomes its own throughput observation before mean, sample SD, RSD, range, and Type A uncertainty are calculated.
Transfer runs—
Mean duration—
Mean throughput—
Throughput sample SD—
Throughput RSD—
Type A uncertainty of mean rate—
Run-by-run transfer precision ledgerUnrounded values drive calculations and decisions
| Run | Duration (s) | Throughput (Mbit/s) | Deviation from mean (Mbit/s) |
|---|
How to use
Measure fixed-payload throughput precision run by run
Analyze repeated end-to-end durations for one unchanged payload. Each duration becomes its own throughput observation before mean, sample SD, RSD, range, and Type A uncertainty are calculated.
- Enter one fixed payload size and exact prefix.
- Paste durations from comparable end-to-end runs.
- Preserve run order.
- Review throughput and duration variation separately.
- Use Type A only as one uncertainty component.
Repeated duration, per-run throughput, spread, and Type A uncertainty
Per-run throughput
Payload bits divided by that run duration.
Sample SD
Observed throughput spread with n − 1 degrees of freedom.
RSD
Spread relative to mean throughput.
Type A
Standard uncertainty of the mean throughput from repeats.
Result interpretation
Read timing and throughput variability as different views
A low RSD shows repeatability for this workload and test window. It does not prove the mean is representative of other file sizes, times, endpoints, paths, protocols, or concurrent loads.
Calculation method
Calculate throughput for each run before summarizing
Normalize the fixed payload to bytes. For each positive duration, calculate payload bits divided by seconds. Calculate arithmetic mean, sample standard deviation, RSD, extrema, and s/√n from the individual throughputs.
Evidence controls
Control caches, endpoints, payload identity, and timer boundaries
Identical transfer boundary
Start and stop timing at the same application, network, or storage boundary for every run.
Cache and warm-up
Cold caches, DNS, authentication, connection setup, and ramp-up can create distinct run populations.
Run order
Preserve sequence to expose thermal throttling, congestion cycles, background load, or adaptation.
Payload stability
Hash or byte-count the object; compressed or sparse representations can alter work.
Independence
Back-to-back runs may share cache and network state and are not automatically independent.
Mean estimator
Mean of per-run throughput differs from payload divided by mean duration. This page uses the former explicitly.
External conditions
Record path, endpoints, protocol, concurrency, CPU, disk, and service state.
Visual explanation
Repeated transfer timelines and throughput deviation strip
Each marker is a throughput derived from one observed duration. The mean and one-SD band expose both center and variability while retaining test order in the ledger.
Detailed calculation process
Recalculate Measure fixed-payload throughput precision run by run from current inputs
Ri = 8B/ti; R̄ = ΣRi/n; sR = √[Σ(Ri − R̄)²/(n − 1)]; uA = sR/√n
| Symbol | Meaning | Required unit |
|---|---|---|
| B | fixed payload size | bytes |
| ti | end-to-end duration for run i | s |
| Ri | payload throughput for run i | bit/s |
| R̄ | mean per-run throughput | bit/s |
| sR | sample SD of throughput | bit/s |
| uA | Type A uncertainty of mean throughput | bit/s |
- Waiting for current inputs.
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Reconciliation:Waiting for current inputs.
Defaults and assumptions
Measure fixed-payload throughput precision run by run starting assumptions
Defaults illustrate six transfers of the same 2 GB payload taking roughly 80 seconds. Replace both the byte count and every duration with one controlled test series.
| Check | Current value A | Current value B | Decision role |
|---|
Decision analysis
Decide whether the repeat set characterizes the required path
Compare mean and variability with workload-specific service objectives. Investigate ordered patterns before combining runs or declaring stable performance.
Do not cherry-pick the fastest run as sustainable throughput. Separate warm-up runs only under a predeclared test protocol and retain them. If duration measurement resolution is coarse relative to short transfers, enlarge payload or repeat batches. For capacity planning, include between-day, path, endpoint, and concurrency variation; this page estimates only repeat precision for one test design. A smaller Type A uncertainty after many nearly identical cached runs may be precise but operationally unrepresentative.
Verification workflow
Validate the repeated-transfer evidence chain
Write a repeatable test protocol
Define exact payload bytes and hash, source and destination, transfer direction, protocol, connection reuse, concurrency, timing events, cache state, warm-up treatment, and environmental controls. Choose whether the test represents cold start, steady state, or routine mixed operation. Record failures and timeouts before collecting data. The same nominal file name is insufficient if compression, sparse allocation, object version, or generated content can change the bytes actually transferred.
Use a consistent timing boundary
Start and stop every run at the same observable events, such as application request to verified payload completion. Distinguish client wall-clock time from server processing or interface counter windows. Use a clock with adequate resolution and known stability. When startup is a material fraction of duration, either retain it as part of the end-to-end measurand or model it separately under a predeclared rule; never subtract it selectively from slow runs.
Inspect order and operating state
Review throughput against run number, time, CPU, disk, cache, network counters, packet loss, and concurrent traffic. Warm-up, congestion cycles, thermal throttling, garbage collection, background jobs, or service adaptation can create trends and clusters. Sorting destroys that evidence. If distinct regimes are real, report them separately with their conditions rather than pooling them into one deceptively wide or narrow standard deviation.
Choose statistics for the decision
This page averages per-run throughputs, which weights each run equally. Aggregate payload divided by aggregate time produces another estimator and may be preferable for a combined work total. Name the estimator before comparing systems. Use sample SD or RSD only when their assumptions and scale are meaningful. For service objectives, percentiles, failure probability, and time-window distributions may be more relevant than Type A uncertainty of a short repeat mean.
Report scope and reproducibility
Archive each duration, derived throughput, payload identity, exclusions, telemetry, and configuration. State whether the result represents one endpoint pair, one hour, one access network, or a broader population. Repeat on different days and under relevant loads before claiming operational stability. A precise cached laboratory transfer can be useful diagnostic evidence but cannot by itself support Internet, disaster-recovery, or multi-user capacity commitments. Preserve the original series whenever the protocol changes.
Evidence and data lineage
Records required for Measure fixed-payload throughput precision run by run
Retain exact bytes and hash, timing clock and boundaries, run order, timestamps, endpoints, route, protocol, connection reuse, cache state, concurrency, CPU and storage telemetry, packet loss, service tier, and exclusions.
Limits and exclusions
Boundaries of Measure fixed-payload throughput precision run by run
The model does not infer causation, remove warm-up, detect outliers, estimate long-term variability, model latency, subtract setup, include failed runs, or predict other payloads and protocols.
Reference framework
Authority for Measure fixed-payload throughput precision run by run
IETF RFC 6349 describes a structured approach to TCP throughput testing and identifies path, window, loss, and host factors that can influence observed performance. The statistical calculations here follow standard sample-mean and sample-standard-deviation definitions, but the test protocol determines what population those statistics represent. Use authoritative protocol and service documentation to define payload, timing, connection, and endpoint boundaries. When reporting network service performance, follow the applicable measurement standard or contract for sampling windows, exclusions, percentiles, and failure treatment. The calculator preserves run evidence; it does not certify the path or replace packet, interface, storage, and host telemetry. Also report unsuccessful attempts, reconnects, and operator interventions under the test protocol. A precision statistic calculated only from completed runs can materially understate service variability when failure is part of normal operation, so completion probability and recovery time should remain separate performance measures. Preserve every timestamp, failure classification, and documented intervention alongside the successful duration series.
Measure fixed-payload throughput precision run by run terminology
RunOne timed transfer observation.
Timing boundaryEvents defining start and finish.
Warm-upEarly behavior before steady state.
CacheStored data that can bypass slower work.
GoodputUseful payload delivered per time.
AutocorrelationDependence between ordered observations.
RSDRelative standard deviation.
Type A uncertaintyStatistical standard uncertainty of the mean.
Worked decision cases
Measure fixed-payload throughput precision run by run decisions in practice
Controlled LAN test
Fixed object and endpoints show low short-term variability after documented warm-up.
Internet service test
Time-of-day and path changes require broader sampling than one repeat series.
Important note
Before relying on Measure fixed-payload throughput precision run by run
Repeatability of one cached workload is not a guarantee of end-to-end production performance.
Measure fixed-payload throughput precision run by run FAQ
Why enter several durations?
Each is one repeat transfer of the same payload; together they quantify variability.
Which number is final?
Mean throughput is the center, while SD, RSD, range, and Type A describe its precision.
Why calculate throughput per run?
The reciprocal relationship means averaging durations and then dividing is a different estimator.
Can payloads differ?
No. Normalize to one fixed byte count or use a batch model.
Should I sort durations?
No. Order may reveal drift or changing network state.
Can failed runs be omitted?
Only under a documented protocol; retain and report exclusions.
Does low RSD mean the link is fast?
No. It means results are consistent, not necessarily high.
Does Type A include clock accuracy?
No. Clock and boundary uncertainty are separate components.
Why use Mbit/s in the ledger?
It is a readable decimal display of bit/s calculations.
Can I compare against advertised rate?
Yes descriptively, but payload boundary and protocol overhead must be aligned.