Musk's plan to buy Twitter has worried policymakers around the world.Musk’s plan to buy Twitter has worried policymakers around the world.

Musk has said that he won't move ahead with his purchase of the micro-messaging service until he knows how many accounts are fake.

In a filing earlier this month, the company estimated that less than 5% of its daily active users were bots.

Around 20% of the accounts on the micro-messaging service are fake, and Musk is concerned that the number could be even higher.

The offer was based on the SEC filing, Musk said early Tuesday morning. The deal cannot move forward until he does.

In pre-market trading on Tuesday, the company's shares slipped 2.22%. A CNBC request for comment was not immediately responded to by a person from the company.

Musk has said his team are conducting their own analysis on the number of fake accounts on the platform, but experts in social media, disinformation and statistical analysis say his suggested approach to further analysis is woefully deficient.

To find out, my team will do a random sample of 100 followers.

Pick any account with a lot of followers and ignore the first 1000 followers, then pick every 10th. I am open to better ideas.

Musk said that he picked 100 as the sample size number for his study because that's the number they use to calculate the numbers in their earnings reports.

Any random sampling process is fine. It will be telling if many people get the same results. The sample size number was 100, because that's what Twitter uses to calculate fake/spam/duplicate.

Carl T. Bergstrom, a University of Washington professor who co-authored a book to help people understand data and avoid being taken in by false claims online, told CNBC that sampling one hundred followers of any single account should not serve as a guide.

He said that a sample size of 100 is much smaller than the norm for social media researchers to study similar issues.

Moskovitz pointed out that Musk's approach is not random, uses too small a sample, and leaves room for massive errors.

Additional reporting by CNBC's Lora Kolodny.