` :Umm, do you work for a tobacco corporation or something? Your 'example' is a non-sequitur.  :Firstly:  ::How can the ``relative risk`` of the population be 1 as you state? What is your exposed and what is your non-exposed group if you take the entire 'population' as a group? Please have a read through the interpretation of RR. There must be a stratification of 'exposed' and 'non-exposed' if you are to use this measure (hence the term 'relative'). Saying that the population has a relative risk of 1 makes as much sense as saying the population has an odds ratio of 1 for bowel cancer - at best, it can be considered a vacuous truth.  :Secondly:  ::Your interpretation of the result is done in a bayesian inference manner (probability that the null hypothesis is true given the data, P(H0 | Data)); however, the study was done using confidence intervals, which is a frequentist approach to inference (given the data, what is the probability that the null hypothesis is true, P(Data | H0)). Therefore, it makes absolutely no sense to draw conclusions as you did. Please have a read at at the different approaches to probabilistic analysis specifically bayesian inference vs frequentist inference.  :Thirdly:  ::You implicitly assumed the prior probability of the null hypothesis H0 as 'vanishingly small' which is incorrect. As the paper you cited is a meta-analysis, it was not a one-off study of a 'random hypotheses' or acquired through 'data mining' approaches to discovery (which could indeed be invalid). Your premise states that ``Of the vast number of things that can be tested, very few actually are connected to coronary heart disease``. This would true if ``second-hand smoking`` was an arbitrary factor such as ``the number of jelly beans I saw at the shopping mall last year``. But you seem to forget/omit that passive smoking is far from just a 'random hypothesis'. In fact, there is significant pre-existing literature regarding the association between passive smoking and cardiovascular disease. Therefore this hypothesis was motivated by existing pathophysiological, experimental and epidemiological knowledge of the aetiology of atheromas and CVD.   :   ::This would clearly affect the 'a priori' probability for the null hypothesis being true, P(H0). Therefore, if you were to use a bayesian approach to the interpretation of this study (which was actually presented with a frequentist approach), you would need to perform bayesian inference and use the prior probability, P(H0), to find the posterior distribution, P(H0 | Data). This would be done using the relationship P(H0 | Data) = [P(Data | H0) * P(H0)] / P(Data). The computations of that is another matter entirely - but suffice to say, the answer would be nothing like your conclusion.  :Fourthly:  ::Note that your 'result' does not even depend on the data, meaning your argument would have been the same even had the confidence interval been [100000, 100100]. Clearly that is dubious.  :All in all, your 'conclusions' about the article are about as valid as your knowledge of statistics.  :If this was an unintentional mistake then I apologize for seeming brash, but there is a fairly large amount of validated, peer-reviewed literature concerning the damaging effects of passive smoking. Perhaps you should go have a look at some meta-analysis on Cochrane?`