![]() Conventionally, we reject the null hypothesis if the probability of its being correct is less than 0.05 or 5%, which is the so-called alpha level. Second, we need to decide the criterion for rejecting the null hypothesis. Once you are done with this example, you may want to continue reading this example on estimating and reporting the effect size following a cluster-based test. 100%) between the two hypotheses can be construed as the effect size, which has great influence on power: statistical power tends to be greater with larger effect size. Only with these parameters specified can data be sampled from the distributions. And under the alternative hypothesis, we will assume that the coin has a chance of 100% to land on head, based on the result our observations so far. In the current example, the chance of a coin to land on head under the null hypothesis is undoubtedly 50%. Using this distribution, we can simulate the outcome of each experiment (i.e., the number of heads out of a certain number of tosses).įirst, we need to “guess”, based usually on pilot studies or prior similar studies, the key parameters of the binomial distributions for each of the null and alternative hypotheses. We know the outcome of tossing a coin conforms to a binomial distribution. Luckily, we don’t really have to toss a coin, as MATLAB can simulate the results for us. However, even a simple task such as coin-tossing will be very time-consuming and tedious when you need to repeat it 5000 times. By definition, we can calculate power by doing the same experiment a large number of times (e.g., 5000 times), and then calculating the proportion of the number of times in which null hypothesis can be rejected to 5000. The power of a statistical test quantifies how sure we can be to decide the coin is unfair (i.e., rejecting the null hypothesis).
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