Interesting. I imagine this method would be particularly helpful for non-anonymized surveys. Obviously, this would only be helpful if you don't have a good ide

Interesting. I imagine this method would be particularly helpful for non-anonymized surveys. Obviously, this would only be helpful if you don't have a good idea of how many people would lie in the first place. It also depends on the assumption that people won't lie given this plausible deniability. The biggest problem I see is that this could only increase the variance of your survey results. The way I see it you have three binomial distributions base on three random variables: 1. The number of people who would answer yes to the survey question if they were honest. 2. The number of people who lied. (This is clearly not independent to the first random variable) 3. The number of people who flipped heads. (This clearly is independent) The problem is that coin flipping has the highest possible variance of any binomial distribution for any given sample size. So even if the variance created by people lying is completely eliminated, it would be more than counteracted by the variance introduced by the coin flipping. I still really like this method since the increased variance is a moot point if giving people plausible deniability is the best way to normalize for lying. And you can always increase your confidence in the resulting proportion by increasing your sample size. It would be interesting to run anonymized and non-anonymized surveys with the coin flip and without to try to determine how much anonymization reduces lying on various survey questions. One nitpick: You should subtract 50% from the yes count and then multiply by two. So in your example, you would expect that 20% of those surveyed truly sexted or use drugs.