What is Statistical Significance? Statistical Significance Explained
Statistical significance refers to the likelihood that an observed result or difference in data is not due to random chance but represents a meaningful relationship or effect. In other words, it helps determine whether the observed findings are statistically meaningful or whether they could have occurred by chance alone.
When conducting statistical hypothesis tests, statistical significance is often assessed by comparing the observed data with a null hypothesis, which assumes that there is no effect or relationship in the population. The p-value is a common measure used to assess statistical significance.
Here are the key points regarding statistical significance:
Null Hypothesis (H0): The null hypothesis is a statement that assumes there is no difference, effect, or relationship in the population. It serves as a baseline against which the observed data is compared. The null hypothesis is typically denoted as H0.
Alternative Hypothesis (H1): The alternative hypothesis is a statement that contradicts the null hypothesis and suggests the presence of a difference, effect, or relationship in the population. The alternative hypothesis is typically denoted as H1.
p-value: The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from the sample data, assuming the null hypothesis is true. It quantifies the strength of evidence against the null hypothesis. If the p-value is below a predetermined threshold, often denoted as the significance level (e.g., α = 0.05), the result is considered statistically significant. In this case, the null hypothesis is rejected in favor of the alternative hypothesis.
Significance Level (α): The significance level, denoted as α, is the threshold chosen to determine statistical significance. It represents the maximum probability of observing a result as extreme as the one observed, assuming the null hypothesis is true. Commonly used significance levels are 0.05 (5%) and 0.01 (1%). A smaller significance level implies a stricter criterion for rejecting the null hypothesis.
Type I and Type II Errors: In hypothesis testing, there are two types of errors that can occur. A Type I error occurs when the null hypothesis is rejected, but it is actually true. This is equivalent to a false positive result. A Type II error occurs when the null hypothesis is accepted, but it is actually false. This is equivalent to a false negative result. The probability of a Type I error is equal to the chosen significance level (α), while the probability of a Type II error is denoted as β.
It is important to note that statistical significance does not imply practical or meaningful significance. A statistically significant result indicates that the observed effect is unlikely to be due to random chance, but it does not necessarily imply that the effect size is large or practically meaningful. Consideration of effect size, practical significance, and the context of the study is crucial when interpreting statistical significance.
Statistical significance is widely used in various fields, including scientific research, social sciences, healthcare, and business, to assess the reliability and validity of study findings and support decision-making processes.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.