I've tried changing mine with foam tips and just third party silicone tips. I did get less discharge but not completely zero. it must be something else.
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Airpods Pro are garbage and Apple will jerk you around on replacement.
- Thread starter FrustratedAppleCustomer
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I think I’ll end up selling mine then. I don’t think the itchiness and potential long term damage is worth the convenience of the APPs. Not sure what to replace them with, however. The Sony’s look the next best thing, but is the Bluetooth connection as stable?
I tried the sonys. for the price, I don't think they're that good. APPs sounded better, had better transparency, better connection and ease of use, and most importantly they were MUCH smaller.
the sonys are super bulky. I returned them. the only nice thing about the sonys is that you can use the app to do some EQ. and since I already have the over-ears, I already have the app installed anyway.
but already connecting them to the phone was a bad experience. for some reason the over-ears (1000xm3) are much easier to connect and predictable (i.e. disconnect from one device, connect to another. easy)
Cheers for that. Yup, the size of the Sony’s is what’s putting me off. Do you know if the og AirPods cause the ear issues? I’m thinking about just getting a pair of them, I’m not really that fussed over ANC.
Mine are great, but they dont stay in the ear as well as normal Airpods. Also, the cracking on noise cancelling is really crap and needs to be solved.
Why are you assuming I haven't done any troubleshooting?
I have spent way too much time troubleshooting them. Including jumping through all the hoops of technical phone support. Unlike rusted-on apple fanbois, I don't spend $250 on a product supposedly backed by a $2T company with the expectation that I will need to waste hours of my own time debugging them.
I expect them to replace them under the warranty I paid for when I bought them. If they can't diagnose issues remotely, that's their problem, not mine. There is no way apple don't know about these issues, and there's no way they don't know that replacing one component at a time often does not fix the problem. They are simply jerking their customers around to save a few bucks.
Assume failure probability is x. Probability of seeing 4/5 failures is 5 * x^4 * (1-x) ~ 5*x^4 for small x. Even assuming the failure probability is an unacceptably high 10%, the probability of seeing 4/5 failures is 0.05%. In other words, a "remarkable hit rate".
The sample size is too small. Period. Statistical probability is only valid with a representative sample size to avoid skewing.
You can quote percentages all you want, but 5 people is a microscopic size as indicated by my representative sample of 4 showing totally different results.
What we both have is a case of a sample size that neither can say with any statistical certainty is representative of the overall population.
As such, that small sample size is no longer able to be used to create any firm statistical conclusion, ergo it can’t.
Welcome to my world of ML...
Lol. If only you knew. If you studied ML at the graduate level, you probably studied my work.
Needless to say, you wouldn't have passed my classes.
Sure, you want to go with that then fine. 5 users does not a representative sample make.
If you believe it does then I’m rather glad I did not study under you.
This IS Apple’s usual quality. Apple doesn’t have any special quality control standards. In fact I’ve never made the level of trouble with ANY brand including Xiaomi as I’ve had with Apple from a reliability or QC/longevity standpoint.
I‘ll take his word that he knows about this stuff better than anybody in this thread.
Newbie joins only to rant about his APP and already you have him an expert on anything.. Ok, then..
You're confusing estimation of probability for a single event with a sample sufficiently large for the implicit multiple hypothesis testing involved in a typical ML scenario. 4/5 failures is extremely low probability for a random sample *if* they're independent and identically distributed samples from the same population with the same low probability of failure. Alternatively, you could take a Bayes approach and estimate the posterior distribution on failure probability given a uniform prior. You'll find that it is Beta(5,2), which looks as follows:
Note the very small mass assigned to low failure probabilities. It's perfectly valid to infer that the failure rate of airpod pros is likely significant.
Since they were all bought at different times and in different places (making it very unlikely that they're from the same "bad batch"), there is no reason to believe otherwise.
Can you prove any of these suppositions, or are they educated assumptions?
What about my sample? I’m in Ohio, wife got mine from Verizon. I got hers from B&H in New York. My sister in in England and my friend is in Cape Town.
All have reported no issues as of the other day when I asked.
How come your sample size of 5 is statistically relevant and my sample size of 4 is apparently not, other than your results work into your personal narrative?
Note, I never claimed anything on their reliability.
Yes, I can prove they were bought in different places at different times.
Your sample of 4 is additional data. Unfortunately, it's biased, given that you are an antagonistic respondent to my original post. However, even if we ignore the bias and simply combine our samples, we have a Beta(5, 6) posterior for the failure probability, which, although better, still assigns very low probability to low failure rates:
Wait, what my 4 is biased because it disagrees with your 5?
Holy heckballs! Talk about making excuses.
I’m done with you on this - you’re now just getting rude and flippant.
Biased in the statistical sense. You claimed to be an ML guy so I assumed you understood the difference between statistical bias and the more pernicious kind. And yes, of course your sample is (statistically) biased. Imagine that the only people who bother to respond to my post are those who disagree and have had wonderful experiences with their APPs. Then the additional samples are not from the underlying distribution of APPs: they're biased towards those APPs that didn't fail. Conversely, I might only get responses from those who agree with me, which would be biased the other way. The problem is, we can't tell. But we do know that you responded because you had a better experience, which automatically makes yours a biased sample. Note again that the "bias" here is non-pejorative; it simply means statistical bias.
[MOD NOTE]
This thread has just devolved into offtopic bickering - locking
This thread has just devolved into offtopic bickering - locking
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