robability, which is often mistaken or misunderstood. The known fact that, in principle, a flipped coin has equal chances to fall on heads or tails provides no clue as to what the outcome will be. We may just observe the coin falling on tails several times in a row, before it finally chooses to fall on heads, as the reader can easily check (try doing the experiment!). Therefore, the meaning of a probability is not the prediction of the outcome (event x being verified) but the measure of how likely such an event is. Therefore, it actually takes quite a number of trials to measure such likelihood: one trial is surely not enough, and worse, several trials could lead to the wrong measure. To sense the difference between probability and outcome better, and to get a notion of how many trials could be required to approach a good measure, let's go through a realistic coin-tossing experiment. First, it is important to practice a little bit in order to know how to flip the coin with a good feeling of randomness (the reader will find that such a feeling is far from obvious!). The experiment may proceed as follows: flip the coin then record the result on a piece of paper (heads = H, tails = T), and make a pause once in a while to enter the data in a computer spreadsheet (it being important for concentration and expediency not to try performing the two tasks altogether). The interest of the computer spreadsheet is the possibility of seeing the statistics plotted as the experiment unfolds. This creates a real sense of fun. Actually, the computer should plot the cumulative count of heads and tails, as well as the experimental PDF calculated at each step from Eq. (1.4), which for clarity I reformulate as follows: number of heads counts number of trials (1.5) pltails)- tails counts number trials The plots of the author's own experiment, by means of 700 successive trials, are shown in Fig. 1.3. The first figure shows the cumulative counts for heads and tails, while the second figure shows plots of the corresponding experimental pr