Legacy has a Family File Statistics<\/em> report that does exactly that. It\u2019s basically a peek under the hood\u2014what\u2019s in the database, where it\u2019s strong, and where it\u2019s still held together with placeholders and good intentions.<\/p>\n Before I dive into the numbers, I should admit something: I\u2019m one of those people who keeps coming back to the Genealogical Proof Standard<\/strong>\u2014not because I enjoy rules for the sake of rules, but because I\u2019ve learned (the hard way) that a family tree is only as strong as the evidence holding it up.<\/p>\n And yes\u2026 I am whining a little about sources.<\/p>\n Because in genealogy, \u201cI saw it on someone\u2019s tree\u201d is not a source. A family story is not a source (even when it\u2019s a wonderful<\/em> story). And a screenshot you can\u2019t re-find later is just a tragedy waiting to happen. If I can\u2019t point to where<\/em> something came from\u2014properly, clearly, so someone else can retrace the steps\u2014then it doesn\u2019t really belong in the tree yet. At best, it\u2019s a clue. At worst, it\u2019s a future error that will quietly multiply.<\/p>\n So how does that ideal look in my own database?<\/p>\n Honestly: it\u2019s a mix of discipline and messy reality. Parts of my tree are rock-solid, anchored in parish registers, censuses, probate records, and the kind of boring-but-beautiful documentation that holds up under scrutiny. But the \u201cunder the hood\u201d statistics also reveal the places where I\u2019ve been\u2026 optimistic. People with incomplete dates. Families that look suspiciously small. A few absurd outliers that practically scream \u201cwrong person\u201d or \u201ctypo.\u201d Those aren\u2019t just database quirks\u2014they\u2019re reminders that proof is a practice, not a personality trait.<\/p>\n In other words: I believe<\/em> in the GPS, I aim<\/em> for good sourcing, and my own statistics politely show me where I\u2019ve still got work to do.<\/p>\n Now for the a look under the hood of my genealogy database.<\/p>\n My file currently holds 12,740 individuals<\/strong>.<\/p>\n That number looks impressive on paper, but here\u2019s the more revealing detail: 2,232 people<\/strong> in the file have insufficient date information<\/em>.<\/p>\n And I actually like that the report says it so plainly, because it confirms what we all know: genealogy databases often grow faster than our documentation does<\/strong>.<\/p>\n It also explains why the \u201cBirths by Era\u201d chart totals 10,508<\/strong>\u2014it\u2019s basically showing the dated portion<\/em> of the tree (12,740 minus 2,232). In other words, the era chart isn\u2019t a perfect picture of my whole database; it\u2019s a picture of the part that has enough dates to behave nicely on a timeline.<\/p>\n Once you look at the dated portion, the center of gravity jumps out:<\/p>\n 1700\u20131799:<\/strong> 4,369<\/p>\n<\/li>\n 1800\u20131899:<\/strong> 3,559<\/p>\n<\/li>\n 1600\u20131699:<\/strong> 1,439<\/p>\n<\/li>\n 1900\u20131999:<\/strong> 983<\/p>\n<\/li>\n Before 1600: just a handful<\/p>\n<\/li>\n<\/ul>\n So if you want the plain truth: my database is a very Norwegian kind of tree<\/strong>\u2014thickest in the centuries where the records are most consistently useful. It also reflects that I have been working extensively on a cluster genealogy for my childhood municipality Vestnes. I started, and am still adding records from the 1700s.<\/p>\n The 1700s and 1800s are where the paper trail tends to hold steady: parish registers, confirmations, marriages, burials, and a growing ecosystem of supporting sources. The 1600s exist, but it\u2019s more \u201ccareful work\u201d and less \u201csteady coverage.\u201d And the 1900s\u2026 well, that\u2019s often where living memory and privacy considerations complicate things.<\/p>\n Now for the part I always enjoy: the \u201cthis can\u2019t be right\u201d numbers.<\/p>\n My report includes:<\/p>\n a 123-year lifespan<\/strong><\/p>\n<\/li>\n and a 156-year marriage<\/strong><\/p>\n<\/li>\n<\/ul>\n If you\u2019ve kept a genealogy database for any length of time, you already know what those numbers are.<\/p>\n They aren\u2019t miracles.<\/p>\n They\u2019re breadcrumbs leading to errors<\/strong>.<\/p>\n Usually it\u2019s something very human:<\/p>\n a death date attached to the wrong person (same-name confusion)<\/p>\n<\/li>\n a typo that shifts a date by a century<\/p>\n<\/li>\n an estimated date entered as a fact<\/p>\n<\/li>\n a duplicate marriage event or mislinked spouse<\/p>\n<\/li>\n<\/ul>\n And here\u2019s the thing: these outliers aren\u2019t just embarrassing\u2014they\u2019re useful<\/em>. Because the stats page hands me a practical task list: fix the biggest outliers, and the whole database becomes more trustworthy<\/strong>.<\/p>\n My file currently has 3,565 marriages<\/strong> where both spouses are recorded.<\/p>\n But I also have 1,380 marriages with an unknown date<\/strong>.<\/p>\n That\u2019s a reminder that even in a big database, a lot of the structure is still \u201cfloating\u201d until we anchor it with dates.<\/p>\n Still, the age patterns for the marriages that are<\/em> dated look familiar:<\/p>\n husbands most commonly in their late 20s and early 30s<\/p>\n<\/li>\n wives most commonly in their early-to-late 20s<\/p>\n<\/li>\n<\/ul>\n That\u2019s not just a statistic\u2014it\u2019s a small window into how family formation often worked in the periods my research covers. These statistics confirms information from other sources about typical ages for marriage.<\/p>\n This one is sneaky, and it\u2019s the part that always feels most personal.<\/p>\n The report shows 9,105 children<\/strong>, and a surprising number of families that appear to have:<\/p>\n one child<\/strong><\/p>\n<\/li>\n or even zero children<\/strong><\/p>\n<\/li>\n<\/ul>\n Now, historically, small families exist. Infant mortality exists. Remarriages exist. Childless couples exist.<\/p>\n But when \u201cone-child families\u201d become the largest category<\/em> in a database, it\u2019s usually not a demographic truth\u2014it\u2019s a workflow truth.<\/p>\n It often means:<\/p>\n I\u2019ve found the couple and the baptism of my direct ancestor, but haven\u2019t swept the parish for siblings yet<\/p>\n<\/li>\n some children are still sitting in my notes, not linked in the file<\/p>\n<\/li>\n a branch is \u201cgood enough for now\u201d but not completed<\/p>\n<\/li>\n<\/ul>\n So the stats page isn\u2019t just reporting history\u2014it\u2019s quietly pointing at the parts of my process<\/em> that are unfinished. This number will improve a bit as I progress with my afore mentioned cluster genealogy.<\/p>\n If you\u2019ve followed my blog for a while, this won\u2019t surprise you: the place list confirms that my tree has a very clear home region.<\/p>\n The most frequent places include:<\/p>\n Vestnes kyrkje \/ Ve\u00f8y prestegjeld<\/strong><\/p>\n<\/li>\n Tresfjord kyrkje<\/strong> Even the \u201ccommon surnames\u201d list reads like a map: Vike, Helland, Vestnes, S\u00e6tre, Aas, Daugstad<\/strong>.<\/p>\n Which is exactly how Norwegian genealogy works. In many periods, what looks like a surname is often just a place name someone carried for a while<\/strong>\u2014and my database statistics reflect that beautifully.<\/p>\n Looking at this report, I\u2019m reminded of three things:<\/p>\n A good tree has a shape.<\/strong> Mine is thick in the 1700s and 1800s because that\u2019s where the records give me the best footing.<\/p>\n<\/li>\n Outliers are not annoyances\u2014they\u2019re leads.<\/strong> The \u201c123-year\u201d people are basically neon arrows saying: check me<\/em>.<\/p>\n<\/li>\n Database stats reveal your habits.<\/strong> The \u201cone-child families\u201d don\u2019t just describe the past; they describe where I still need to do the unglamorous sweep work.<\/p>\n<\/li>\n<\/ol>\n So yes\u2014this is a look under the hood. Not just at my ancestors, but at my own research workflow.<\/p>\n And if you\u2019ve never run a statistics report on your own tree, I recommend it. It\u2019s one of the few times your genealogy software stops being a storage box and becomes something better:<\/p>\n A mirror.<\/p>\n So that\u2019s the view under the hood: not just a pile of names, but a living research project with strong beams, a few loose boards, and some parts still waiting to be finished properly.<\/p>\n If anything, the statistics report reminds me why the Genealogical Proof Standard matters in everyday practice. It\u2019s easy to feel \u201cdone\u201d once a person is added. But the real work is making sure each connection is supported by sources someone else could follow\u2014and that the dates, places, and family structures actually make historical sense.<\/p>\n For me, the next steps are simple and old-fashioned: hunt down missing dates, clean up the outliers that can\u2019t be true, and go back to those suspiciously small families to see what the records really say. Because the goal isn\u2019t a bigger tree. It\u2019s a sturdier one.<\/p>\n Maybe your genealogy software has a similar function and this article can be a inspiration to take a look at the strengths and possible weaknesses of your database.<\/p>\n
\nThe headline: a big tree\u2026 with an honest gap<\/h2>\n
\nWhere my tree really \u201clives\u201d: the 1700s and 1800s<\/h3>\n
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\nThe stats that made me laugh (and then open the file)<\/h2>\n
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\nMarriage patterns: the tree behaves like history<\/h2>\n
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\nChildren per family: a quiet clue about what I haven\u2019t finished yet<\/h2>\n
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\nNames and places: this database has a strong geographic heartbeat<\/h2>\n
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\nand a set of farm names that show up again and again.<\/p>\n<\/li>\n<\/ul>\n
\nWhat I take away from this (and maybe you will too)<\/h2>\n
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\nConclusion<\/h2>\n
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