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the-pulse/posts/2026/07/31/DQUL_July.qmd

@@ -10,7 +10,7 @@ author: Tom Redman, Roger Hoerl and Hannah de Mowbray
 date: 2026-07-31
 date-format: long
 toc: true
-image: images/table1.WEBP
+image: images/census.jpg
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 *Data Quality Under the Lens is a new Real World Data Science column. Each edition explores real-world moments where data quality shaped outcomes, sometimes driving failure, sometimes preventing it. From near misses to hard lessons learned, we look at what happens when data is up to the task... or falls short.* 
@@ -44,13 +44,13 @@ Importantly, six states—Colorado, Minnesota, Rhode Island, Florida, Texas and
 
 There were twenty-one states with large miscounts, with ten states having large undercounts and eleven large overcounts.
 
-![](https://realworlddatascience.net/images/table1.WEBP)
+![](https://realworlddatascience.net/images/table1.webp)
 
 Conducting a census is difficult (try simply counting your socks!) and the pandemic added enormous challenges. One should expect problems. Further, as we’ve already noted, the census is used for many purposes. But none of this means that the central issue for representative democracy can be overlooked. 
 
 Had each state simply been over- or undercounted by a similar proportion, apportionment issues would likely have been minor. But the state-to-state variation is large, especially compared to the 2010 census. In 2010, estimated miscount rates were tightly clustered near zero, while for 2020 they were much more dispersed, which is especially clear looking at the below chart:
 
-![](https://realworlddatascience.net/images/fig1.WEBP)
+![](https://realworlddatascience.net/images/fig1.webp)
 
 Ultimately, the high state-to-state variation and high miscount rates led to the different numbers of representatives officially apportioned and those projected using PES estimated state populations.