Interpretation rule: Statistical Analysis Dashboard summarizes the numbers you enter. It does not repair weak measurements, make a convenience sample representative, or turn an association into a causal conclusion.
Statistical Analysis Dashboard Guide for FFMI Data
Fat-Free Mass Index data can look deceptively simple because each observation is only one number. The challenge begins when several observations need to be summarized. A coach may have twenty clients, a researcher may extract values from a paper, or an athlete may collect repeated measurements across phases. The Statistical Analysis Dashboard organizes those values so that center, variability, percentiles and group differences can be reviewed together rather than in isolation.
Before interpreting any output, confirm that the FFMI values were created consistently. Raw and height-normalized FFMI should not be mixed without clear labeling, and different body-fat methods can create artificial variation. If you need individual values, start with the FFMI Calculator. For repeated measurements, use the Measurement Log to keep methods and supporting metrics together.
How to Use the Statistical Analysis Dashboard
The workflow is intentionally simple: enter one FFMI dataset, add a second only when comparison is meaningful, choose context, and run the analysis. The Statistical Analysis Dashboard accepts values separated by commas, spaces, tabs, semicolons or new lines, which makes it practical to paste a column copied from a spreadsheet. The calculator rejects nonnumeric tokens rather than silently pretending they were valid observations.
Prepare one consistent column
Use one FFMI definition, one population or clearly defined sample, and consistent units. Remove headers such as “FFMI” before pasting.
Check provenance
Record whether values came from DEXA, BIA, skinfold estimates, a study table or repeated personal measurements.
Analyze Group A first
Read mean, median, SD, IQR and the histogram before deciding whether a group comparison adds useful information.
Interpret with context
Compare the result with the correct sport, sex, age and measurement context instead of applying a universal cutoff.
If you are building a reference set, pair the Statistical Analysis Dashboard with FFMI Distribution Charts and Age-Adjusted FFMI Norms. Those pages can provide population context that a private dataset alone cannot supply.
Mean and Median in the Statistical Analysis Dashboard
The mean is the arithmetic average: add every observation and divide by the sample size. It uses all values, so one unusually high or low observation can move it. The median is the middle ordered observation, or the average of the two middle observations when the sample size is even. Because the median depends on rank rather than the distance of every value from the center, it is usually less sensitive to extreme observations.
Core location formulas
Use both when distribution shape matters. A large gap between mean and median is a prompt to inspect the histogram and source data, not an automatic diagnosis of a problem.
The Statistical Analysis Dashboard reports both measures because they answer related but different questions. In a compact, roughly symmetric sample they may be close. In a skewed sample, the difference can be informative. Neither measure tells you whether the dataset is representative of a broader athlete population.
Sample Standard Deviation, Range and IQR
Variability describes how dispersed the observations are. Sample standard deviation uses the squared distances from the mean and the n−1 denominator. Range is simply maximum minus minimum. Interquartile range is Q3 minus Q1, so it focuses on the middle half of the ordered sample and is less influenced by the most extreme observations than the full range.
Spread formulas used here
The coefficient of variation expresses SD relative to the mean. That can make relative dispersion easier to discuss when two positive-valued datasets have different means, but it should not be treated as a universal quality score. The Statistical Analysis Dashboard leaves CV undefined when the mean is zero because the ratio would not be meaningful.
Quartiles and Percentiles: Reading Position in a Dataset
Percentiles describe positions after values are sorted. The dashboard reports P10, Q1, median, Q3 and P90. It uses linear interpolation based on (n−1)×p, so a percentile can fall between observations. Other software may use another percentile convention, especially noticeable in short datasets.
Percentiles are especially useful when a distribution is not well summarized by one average. However, a percentile inside your own ten-person dataset is not automatically a population percentile. For external comparisons, use a relevant published reference such as the site’s FFMI for Different Sports resource and confirm that the measurement and population definitions are comparable.
Standard Error and the 95% Mean Interval
Standard error describes how the sample mean would be expected to vary across repeated samples under a statistical model. It is calculated as sample SD divided by the square root of n. The Statistical Analysis Dashboard then applies a two-sided Student’s t critical value to create a 95% interval around the mean when at least two observations are available.
Mean-uncertainty calculations
This interval is a model-based estimate, not a guarantee that 95% of future FFMI observations will lie inside it. Small samples, dependence, non-random sampling and strong non-normality can limit the interpretation.
The Statistical Analysis Dashboard uses exact tabulated two-sided 95% t critical values for 1–30 degrees of freedom and a close large-sample approximation above that range. If you are performing formal research, use the statistical method specified by your study design rather than relying on a general web dashboard.
Histogram and Skewness: Looking at Distribution Shape
A histogram groups observations into intervals so that clusters, gaps and broad asymmetry are easier to see. The Statistical Analysis Dashboard chooses a practical number of bins using a Sturges-style rule and caps the display for readability. Histograms are descriptive: a small sample can look irregular simply because there are few observations.
The dashboard also reports adjusted sample skewness when at least three values are available and sample SD is above zero. Positive skewness suggests a longer right tail; negative skewness suggests a longer left tail. A skewness value is not a substitute for examining the actual measurements, especially when one questionable entry can dominate a short series.
Comparing Two Groups With the Statistical Analysis Dashboard
Add Group B only when the comparison has a clear question, such as two teams measured with the same protocol or two published samples using compatible FFMI definitions. The Statistical Analysis Dashboard reports each group’s sample size, center and spread, then calculates Group A mean minus Group B mean.
Cohen’s d standardizes that mean difference by a pooled sample standard deviation when each group has at least two observations and the pooled SD is greater than zero. A positive d means Group A has the higher mean; a negative d means Group B has the higher mean. Effect size is descriptive here. It does not provide a p-value, prove statistical significance, correct confounding, or show why the groups differ.
Comparison check: Before interpreting Cohen’s d, ask whether the groups are independent, measured comparably and drawn from populations that make sense to contrast. A mathematically correct effect size can still answer the wrong question.
Repeated FFMI Measurements Need Special Care
If one athlete contributes many monthly measurements, those observations are not the same as data from many independent athletes. They share the same person, measurement habits, training history and biological characteristics. You can still use the Statistical Analysis Dashboard to describe the series, but the sample size should not be presented as though every row were a different participant.
For individual progress, use Progress History to keep check-ins in chronological context. When the goal is a client-level review rather than a group distribution, the Client FFMI Assessment provides a more appropriate starting point.
Outliers, Impossible Values and Measurement Quality
An outlier is not automatically an error. A very high or low FFMI observation may be real, may come from a different population, or may reflect a unit, transcription or body-fat estimation problem. The Statistical Analysis Dashboard warns about nonpositive or unusually large entered values but does not delete observations automatically. Removing data simply because it makes a result look cleaner can bias the summary.
Investigate the source first. Check height units, body weight units, body-fat percentage, formula choice and whether normalized or raw FFMI was used. If the issue is measurement quality, review FFMI Measurement Accuracy. A consistent protocol usually improves longitudinal interpretation more than extra decimal places.
Using the Statistical Analysis Dashboard With Research Data
Research papers can report mean with SD, median with IQR, confidence intervals or model-adjusted estimates. These summaries are not interchangeable. Pasting values into the Statistical Analysis Dashboard can help exploration, but it does not reproduce a paper’s study design or inferential analysis.
For source-based FFMI work, use the FFMI Studies Repository to locate study context before extracting numbers. For a general reference on exploratory data analysis and statistical summaries, the NIST/SEMATECH e-Handbook of Statistical Methods is an authoritative external resource. The Statistical Analysis Dashboard should support careful reading, not replace it.
Common Statistical Analysis Dashboard Mistakes
- Mixing raw and normalized FFMI. Label the formula before combining values.
- Combining incompatible body-fat methods without noting the change. Method shifts can create artificial spread.
- Reporting only the mean. Include SD, IQR or another spread measure so readers can see variability.
- Treating a sample percentile as a population percentile. Your local ranking may not generalize.
- Calling a confidence interval a prediction interval. They answer different questions.
- Using Cohen’s d as proof of significance or causation. It is a standardized descriptive difference here.
- Deleting unusual values without checking the source. Investigate data quality before excluding observations.
- Counting repeated observations as independent people. Dependence affects inference and interpretation.
- Ignoring sample selection. A precise summary of a biased sample is still a biased description of a broader population.
- Using FFMI statistics to infer drug use. A distribution cannot determine an individual’s enhancement status.
What the Statistical Analysis Dashboard Cannot Tell You
The Statistical Analysis Dashboard cannot diagnose health, establish an athlete’s natural status, identify a genetic ceiling, prove why one group differs from another, or guarantee that a future observation will match the current pattern. Descriptive statistics summarize the data you provide; they do not create evidence that was absent from the measurement process or sampling design.
It also cannot correct a convenience sample into a representative one. Ten carefully measured athletes from one club may be an excellent description of those ten athletes while still being a weak basis for claims about an entire sport. Statistical precision, measurement validity and external validity are separate concerns.
Best-practice use: Let the Statistical Analysis Dashboard describe what you measured. Then use relevant reference data, measurement notes, study design and performance context to decide what the pattern actually means.