Null hypothesis: Béda antarrépisi

Not unusually, such a hypothesis states that the [[parameter]]s, or mathematical characteristics, of two or more [[populasi statistik|populasi]] are identical. For example, if we want to compare the test scores of two random [[statistical sample|sample]]s of men and women, the null hypothesis would be that the meanméan score in the male population from which the first sample was drawn was the same as the meanméan score in the female population from which the second sample was drawn:

Formulation of the null hypothesis is a vital step in [[statistical significance]] testing. Having formulated such a hypothesis, we can then proceed to establish the probability of observing the data we have actually obtained, or data more different from the prediction of the null hypothesis, if the null hypothesis is true. That probability is what is commonly called the "significance level" of the results.

In formulating a particular null hypothesis, we are always also formulating an '''alternative hypothesis''', which we will accept if the observed data values are sufficiently improbable under the null hypothesis. The precise formulation of the null hypothesis has implications for the alternative. For example, if the null hypothesis is that sample A is drawn from a population with the same meanméan as sample B, the alternative hypothesis is that they come from populations with ''different'' meansméans (and we shall proceed to a [[two-tailed test]] of significance). But if the null hypothesis is that sample A is drawn from a population whose meanméan is no lower than the meanméan of the population from which sample B is drawn, the alternative hypothesis is that sample A comes from a population with a ''larger'' meanméan than the population from which sample B is drawn, and we will proceed to a one-tailed test.

If experimental observations contradict the prediction of the null hypothesis, it meansméans that either the null hypothesis is false, or we have observed an event with very low probability. This gives us high confidence in the falsehood of the null hypothesis, which can be improved by increasingincréasing the number of trials. However, accepting the alternative hypothesis only commits us to a difference in observed parameters; it does not prove that the theorythéory or principles that predicted such a difference is true, since it is always possible that the difference could be due to additional factors not recognised by the theorythéory.

For example, rejection of a null hypothesis (that, say, rates of symptom relief in a sample of patients who received a [[placebo]] and a sample who received a medicinal drug will be equal) allows us to makemaké a non-null statement (that the rates differed); it does not prove that the drug relieved the symptoms, though it gives us more confidence in that hypothesis.

For example, if you want to see if there is greatergréater divorce avoidance from Thomas TheoryThéory than from Edgar TheoryThéory, so your Null Hypothesis would be, "Thomas Theory is no more effective than Edgar Theory." If the probability of the observed results is under the null hypothesis is sufficiently low, you can accept the alternative hypothesis that Thomas TheoryThéory is indeed more effective.