The primary object of statistical analysis is to find out whether the effect produced by a compound under study is genuine and is not due to chance. Hence, the analysis usually attaches a test of statistical significance. First step in such a test is to state the null hypothesis. In null hypothesis (statistical hypothesis), we make assumption that there exist no differences between the two groups. Alternative hypothesis (research hypothesis) states that there is a difference between two groups.
For example, a new drug ‘A’ is claimed to have analgesic activity and we want to test it with the placebo. In this study, the null hypothesis would be ‘drug A is not better than the placebo.’ Alternative hypothesis would be ‘there is a difference between new drug ‘A’ and placebo.’ When the null hypothesis is accepted, the difference between the two groups is not significant. It means, both samples were drawn from single population, and the difference obtained between two groups was due to chance. If alternative hypothesis is proved i.e. null hypothesis is rejected, then the difference between two groups is statistically significant. A difference between drug ‘A’ and placebo group, which would have arisen by chance is less than five percent of the cases, that is less than 1 in 20 times is considered as statistically significant (P < 0.05). In any experimental procedure, there is possibility of occurring two errors.
This is also known as α error. It is the probability of finding a difference; when no such difference actually exists, which results in the acceptance of an inactive compound as an active compound. Such an error, which is not unusual, may be tolerated because in subsequent trials, the compound will reveal itself as inactive and thus finally rejected. For example, we proved in our trial that new drug ‘A’ has an analgesic action and accepted as an analgesic. If we commit type I error in this experiment, then subsequent trial on this compound will automatically reject our claim that drug ‘A’ is having analgesic action and later on drug ‘A’ will be thrown out of market. Type I error is actually fixed in advance by choice of the level of significance employed in test. It may be noted that type I error can be made small by changing the level of significance and by increasing the size of sample.
This is also called as β error. It is the probability of inability to detect the difference when it actually exists, thus resulting in the rejection of an active compound as an inactive. This error is more serious than type I error because once we labeled the compound as inactive, there is possibility that nobody will try it again. Thus, an active compound will be lost. This type of error can be minimized by taking larger sample and by employing sufficient dose of the compound under trial. For example, we claim that drug ‘A’ is not having analgesic activity after suitable trial. Therefore, drug ‘A’ will not be tried by any other researcher for its analgesic activity and thus drug ‘A’, in spite of having analgesic activity, will be lost just because of our type II error. Hence, researcher should be very careful while reporting type II error.