how hypothesis testing works
in hypothesis testing an analyst tests a statistical sample intending to provide evidence on the plausibility of the null hypothesis statistical analysts measure and examine a random sample of the population being analyzed all analysts use a random population sample to test two different hypotheses the null hypothesis and the alternative hypothesis the null hypothesis is usually a hypothesis of equality between population parameters e g a null hypothesis may state that the population mean return is equal to zero the alternative hypothesis is effectively the opposite of a null hypothesis thus they are mutually exclusive and only one can be true however one of the two hypotheses will always be true the null hypothesis is a statement about a population parameter such as the population mean that is assumed to be true 14 step processexample of hypothesis testingif an individual wants to test that a penny has exactly a 50 chance of landing on heads the null hypothesis would be that 50 is correct and the alternative hypothesis would be that 50 is not correct mathematically the null hypothesis is represented as ho p 0 5 the alternative hypothesis is shown as ha and is identical to the null hypothesis except with the equal sign struck through meaning that it does not equal 50 a random sample of 100 coin flips is taken and the null hypothesis is tested if it is found that the 100 coin flips were distributed as 40 heads and 60 tails the analyst would assume that a penny does not have a 50 chance of landing on heads and would reject the null hypothesis and accept the alternative hypothesis if there were 48 heads and 52 tails then it is plausible that the coin could be fair and still produce such a result in cases such as this where the null hypothesis is accepted the analyst states that the difference between the expected results 50 heads and 50 tails and the observed results 48 heads and 52 tails is explainable by chance alone 