This post started out seeking to confirm or debunk the claim located here.

The method was very simple because we have a continuous stream of samples from before COVID, before the COVID vaccination and after the majority of people uploading samples would have been vaccinated. If this massive change is happening then the pre-COVID bifidobacterium count (by lab) would be much higher than the post-COVID vaccination bifidobacterium counts.

My results: there was no statistical significance between the averages

• Pre 2020-01-01: Average Count 20380 on 118 samples, Std Dev 98300
• Post 2022-06-01: Average Count 26111 on 406 samples, Std Dev 72700

That is a 28% increase when a decrease was expected from the above talk.

I am open data, so you can pull the data and check the calculations:

Volatility of Numbers

I was also curious to see if there was any apparent month by month pattern, so I pulled the statistics for biidobacterium, shown below. It is illuminating to a statistician like me, perhaps confusing or concerning to people with poor understanding of statistics (who would expect the numbers from month to month to be similar).

		Thryve			BiomeSight
Year	Month	Average	Std Dev	Obs	Average	Std Dev	Obs
2020	7	32438	131646	24	27929	35826	14
2020	8	25456	43405	21	7683	8948	9
2020	9	13410	19329	17	18501	22566	14
2020	10	84056	148144	18	4370	7390	20
2020	11	18598	34049	9	4926	8197	13
2020	12	10078	17108	16	1841	2718	29
2021	1	68152	172405	20	12436	20675	32
2021	2	101600	163980	30	17289	55509	45
2021	3	57957	103248	17	14482	33774	33
2021	4	21979	42967	30	7700	24436	46
2021	5	24693	51744	56	14257	33608	38
2021	6	28166	84491	39	21465	85762	51
2021	7	47023	105209	39	22620	67229	51
2021	8	60283	82398	43	18427	79784	37
2021	9	62438	92929	28	12002	19635	41
2021	10	13121	29924	24	5922	8565	38
2021	11	11515	27095	57	9996	24966	58
2021	12	28582	80191	17	11498	29919	63
2022	1	15114	28760	38	7076	15149	50
2022	2	24816	59069	32	11707	27202	52
2022	3	10486	23995	33	20243	51539	47
2022	4	10207	21580	57	7916	18288	69
2022	5	33471	82497	80	10304	23719	81
2022	6	23861	60126	53	8053	21994	235
2022	7	26797	109435	40	10709	20439	62
2022	8	67707	132108	60	8085	17190	85
2022	9	13926	17622	28	12635	21332	92
2022	10	9090	14049	45	9627	21171	89
2022	11	10296	19034	39	7293	12150	61
2022	12	3186	6194	21	10887	21390	42
2023	1	10224	18215	41	13302	21612	89
2023	2	10604	22883	52	9103	19781	72
2023	3	65953	78173	30	13566	34256	57

My conclusion is that you need to have two things to get good results:

• All of the samples should be processed by the same lab at the same time. Different batches of reagents may cause different results.
• You need good sample sizes, at least 100+
• You need to be very very careful not to cherry pick data (example below)

An example from Thryve/Ombre data above, with a sample size of 30, the average was 101600. Later a sample size of 21 reported just 3186. Conclusion: going back to school caused family bifidobacterium to tank!

On sample size of 100 issue: