一個新的研究報告稱：健康統計報告對幫助醫生和患者有重大意義。這個報告顯示盲目的統計是給醫療保健和社會帶來負面影響的重大問題。
政客用統計報告來拉選票，制藥公司用統計報告來銷售產品，媒體用統計報告來做所有的事情，但是他們中很少有人真正懂得健康統計。大多數醫生每天都要利用統計數據來做醫療判斷，比如去計算藥物和外科手術的風險，基本統計數字是必須的。
統計所帶來的結論有兩面性，研究者稱統計結果被混淆的原因是統計學是一般傳遞健康信息，在客戶健康信息的處理中會缺少統計學的技巧。
這個結合可以是爆炸性的，英國在九十年代中期一個的報告顯示新的口服避孕藥顯示出不斷增加的血栓風險，這在婦女間引發了恐慌。很多人停止了使用這種避孕方式。在這個報告發表之后，估計13，000甚至更多的墮胎出現在英國和威爾士。公眾如果被告知這種新藥所帶來的血栓風險只是增加了七千分之一或者二，那么這個恐慌就不會發生了。
這個報告的作者同樣引用研究顯示，當數據顯示在一個典型的概率模型時，醫生不能準確的解讀癌癥篩查數據和其他的測試結果，。比如當只顯示一個概率時，一組有經驗的醫師也不能準確地估計測試結果正常而實際上患有結腸癌病人的機會。而當數據顯示在一個自然頻率在一個透明的模型中時大多數醫生給出了正確的答案。

 

Politicians use them to persuade voters, drug companies to sell products, and the media spin them in all kinds of ways, but few really understand health statistics. Even most doctors, who must make daily decisions based on numerical data - for instance, to calculate the risks of a drug or surgical intervention - lack the basic statistical literacy required.

The problem of statistical illiteracy has two sides, the researchers say. It is caused by the confusing ways statistics are ordinarily presented in health communication, coupled with a lack of statistical thinking skills among consumers of health information.

The combination can be explosive. A mid-1990s report in Britain showed that new oral contraceptive pills posed a twofold (or 100 percent) increased risk of blood clots led to a panic among women. Many stopped using this form of birth control. There were an estimated 13,000 more abortions in England and Wales in the year following the report. Had the public been informed that the absolute risk with the new pills only increased from one in 7,000 women having a blood clot to two in 7,000, the panic would not have occurred.

Health statistics are often presented in relative risks (percentages), which are generally large numbers that capture people's attention. Percentages are misleading though when facts like base rates are left out. Absolute risk figures (like “one in 7,000") are much more understandable, and less easy to sensationalise.
The authors of the report also cite studies showing the inability of doctors to accurately interpret data from cancer screenings or other test results, when the data were presented in the typical form of conditional probabilities. For example, when presented with conditional probabilities, a group of experienced physicians was unable to accurately estimate the chances a patient with a positive test result actually had colorectal cancer, given the known sensitivity and false-positive rate of the test used (estimates ranged from a one percent to a 99 percent chance, with most guessing around 50 percent). Most physicians gave the right answer (a less than ten percent chance) when the data were presented in the more transparent form of natural frequencies.