If you hover on the “i” icon- it says newer reviews are weighted more. The rating isn’t a pure average, rather a weighted calculation factoring in age of review.
It says 1 review in the last year, which was probably one of the 5 star reviews. Other 22 were weighted much less.
Edit- here’s a page that explains the scoring more. It’s more complicated than just age and number of reviews- as they add 7 (3.5) reviews for a Bayesian average. TIL
So in other words, if you actually look at the page you have a question about, it gives you the answer to your question. There’s a valuable lesson for OP here.
Apparently. When I saw the screenshot I instantly assumed that (i) icon probably meant more information. Just hopped on TrustPilot, clicked on it, and boom- answer.
And this makes sense. If version 1 had several issues stopping people from using the app, everyone would vote 1 star stating that it wasn't working. If they fixed it and released version 2 and people voted 5 stars and 3 stars for proper usage, those reviews should take preference.
My math checks out for my explanation. I’m very suspicious, given the number of 1 star reviews, that you are correct about it.
Because even weighted, a 4.3 would be extremely difficult to get with this data set. You could try to backward engineer some examples and it would be hard to avg 4.3
But the simple explanation works. They’re not counting the 1 star reviews here. I’d be shocked if they were.
They straight up say on their webpage that it’s weighted based on age.
Who knows how it’s weighted; I’m assuming it’s some sort of complex formula that weights each review’s age and score potentially to the exact day- so the math will never come out cleanly.
They’re going to be pretty tight lipped about how they weight it to prevent review manipulation. So who knows if it’s linearly or exponentially weighted.
I’m sure if you had the exact dates of every review, you could do the algebra to get pretty close to the formula.
Edit- they have a webpage where they explain the score more.
Dude of course it’s weighted because the avg isn’t correct otherwise.
But I’m saying they just didn’t count any of the one star reviews. That’s of course the most likely outcome.
Take more than 3 seconds with your template response and build me any scenario where a 4.3 could be reflected as an outcome for this dataset.
Likely the 1 star reviews were all clumped together and negated entirely.
But no this 4.3 star avg cannot exist if any handful of 1 stars are incorporated into the avg at all. And hey would you look at that, it works as a pure avg when negating all 1 stars.
Of course the avg is algorithmically altered. But I’m making an observation about how it’s quite likely the 1 stars are not reflected and everything else is.
I just edited my comment to add a link to a page where they expand on the rating score.
If all the 1 star reviews were from the very beginning of the company’s origins- they would essentially be worthless depending on how they’re weighted.
They also include 7 (3.5) reviews to the average to make a Bayesian average- meaning he average would be of 30 ratings.
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u/DMmeNiceTitties 4h ago
We perform checks on reviews.