Changes

Empirical (or experimental) probability is calculated simply as the ratio of ''observed'' favourable outcomes to total samples taken. Thus a pair of dice might be rolled a thousand times, with an observed occurrence of 26 pairs of sixes. This gives an empirical probability of 26/1000 or 2.60%. With a larger number of samples, the empirical probability would be expected to tend towards the theoretical value. If it does not, then either the experimental [[methodology]] or the theoretical calculation and its assumptions would be questioned. In the case of the dice, the most likely explanation would be that the dice in the experiment were not truly unbiased.<ref>http://regentsprep.org/Regents/math/ALGEBRA/APR5/theoProp.htm</ref>

Empirical (or experimental) probability is calculated simply as the ratio of ''observed'' favourable outcomes to total samples taken. Thus a pair of dice might be rolled a thousand times, with an observed occurrence of 26 pairs of sixes. This gives an empirical probability of 26/1000 or 2.60%. With a larger number of samples, the empirical probability would be expected to tend towards the theoretical value. If it does not, then either the experimental [[methodology]] or the theoretical calculation and its assumptions would be questioned. In the case of the dice, the most likely explanation would be that the dice in the experiment were not truly unbiased.<ref>http://regentsprep.org/Regents/math/ALGEBRA/APR5/theoProp.htm</ref>

In situations more complex than a pair of dice, the basis of theoretical probability calculations can become increasingly contentious, and the approach tends to be useful only in the case of limited or absent observational data. For example, when the outbreak of a new strain of [[influenza]] occurs, theoretical calculations of expected mortality rates are quickly produced based on extrapolation from other flu strains. These are soon superseded by actual mortality observations.<ref>http://seedmagazine.com/content/article/overestimating_avian_flu/</ref>  

In situations more complex than a pair of dice, the basis of theoretical probability calculations can become increasingly contentious, and the approach tends to be useful only in the case of limited or absent observational data. For example, when the outbreak of a new strain of [[influenza]] occurs, theoretical calculations of expected mortality rates are quickly produced based on extrapolation from other flu strains. These are soon superseded by actual mortality observations.<ref>http://seedmagazine.com/content/article/overestimating_avian_flu/</ref>

 

 

 

[[Probabilistic thinking]] is a way of approaching situations and potential problems by considering the likelihood of a different range of outcomes on a continuum rather than assuming a single, certain outcome. It involves acknowledging uncertainty, estimating probabilities, and making sound and wise decisions based on expected value. This approach can lead to more informed decisions and better management of [[risk]] in various aspects of life.<ref>[https://www.google.com/search?q=probabilistic+thinking&oq=probabilistic+thinking&gs_lcrp=EgZjaHJvbWUyCQgAEEUYORiABDIHCAEQABiABDIHCAIQABiABDIICAMQABgWGB4yCggEEAAYChgWGB4yCAgFEAAYFhgeMggIBhAAGBYYHjIICAcQABgWGB4yCAgIEAAYFhgeMgYICRAuGEDSAQg4NjIyajBqMagCALACAA&sourceid=chrome&ie=UTF-8 Probabilistic thinking]</ref>

==Theory of Evolution==

==Theory of Evolution==