Final answer:
Statistical significance is achieved when the p-value is less than the predetermined level of significance (alpha), leading to the rejection of the null hypothesis and suggesting significant support for the alternative hypothesis. This concept is key in controlling the probability of committing a Type I error during hypothesis testing.
Step-by-step explanation:
An experimental result is said to be statistically significant when b. The null hypothesis is rejected. This occurs when the p-value obtained from the statistical test is less than the predefined level of significance (also known as alpha or α). If α is set at 0.05, for example, and we obtain a p-value of 0.03, we reject the null hypothesis because the p-value is less than 0.05. Rejecting the null hypothesis suggests that there is enough evidence to support the alternative hypothesis, which states that there is a statistically significant effect or difference.
A type I error occurs when we reject the null hypothesis even though it is actually true. Conversely, a type II error occurs when we fail to reject the null hypothesis even though it is false. The decision to reject or not reject the null hypothesis is systematic and involves comparing the p-value to α. When the p-value is greater or equal to the alpha value, we do not reject the null hypothesis, implying that there is not enough evidence to support the alternative hypothesis.
Statistical significance is crucial in research as it helps in determining whether an effect or difference observed in the data is due to chance or a real underlying effect. Establishing the predetermined α helps researchers control for the probability of a Type I error when conducting hypothesis testing, which is central to making statistical conclusions.