• Hypothesis: Unproven propositions about some phenomenon of interest.

Reason:

Hypothesis actually means the hypothetical or imaginary statement about the population which is not known to be true and has to be checked to be correct or false. Thus, it is an unproven statement about a phenomenon.

• Alternative Hypothesis: The hypothesis that a proposed result is not true for the population.

Reason:

whenever the proposed result is false, the hypothesis that comes into the picture is the alternative hypothesis. Thus, the hypothesis that is used when the given statement is not true is the alternative hypothesis.

• Significance level: In a statistical test, the acceptable level of error is selected by the investigator.

Reason:

The level of significance or the significance level is the measure of the type 1 error. Thus, it is the level of type 1 error that is fixed and the acceptable range of error beyond which test hypothesis is rejected.

• p-value: In a statistical test, the probability of obtaining the result if, in fact, the null hypothesis was true in the population.

Reason:

The p-value is nothing but the probability that the sample values comply with the null hypothesis. Thus, if this probability is very small, the null hypothesis is rejected. Thus, the p-value is the probability value that the null hypothesis may be true.