Level of Significance

Statistics is a branch of Mathematics. It deals with gathering, presenting, analyzing, organizing and interpreting the data, which is usually numerical. It is applied to many industrial, scientific, social and economic areas. While a researcher performs research, a hypothesis has to be set, which is known as the null hypothesis. This hypothesis is required to be tested via pre-defined statistical examinations. This process is termed as statistical hypothesis testing. The level of significance or Statistical significance is an important terminology that is quite commonly used in Statistics. In this article, we are going to discuss the level of significance in detail.

Statistical Significance Meaning

In Statistics, “significance” means “not by chance” or “probably true”. We can say that if a statistician declares that some result is “highly significant”, then he indicates by stating that it might be very probably true. It does not mean that the result is highly significant, but it suggests that it is highly probable.

Level of Significance Definition

The level of significance is defined as the fixed probability of wrong elimination of null hypothesis when in fact, it is true. The level of significance is stated to be the probability of type I error and is preset by the researcher with the outcomes of error. The level of significance is the measurement of the statistical significance. It defines whether the null hypothesis is assumed to be accepted or rejected. It is expected to identify if the result is statistically significant for the null hypothesis to be false or rejected.

Level of Significance Symbol

The level of significance is denoted by the Greek symbol α (alpha). Therefore, the level of significance is defined as follows:

Significance Level = p (type I error) = α

The values or the observations are less likely when they are farther than the mean. The results are written as “significant at x%”.

Example: The value significant at 5% refers to p-value is less than 0.05 or p < 0.05. Similarly, significant at the 1% means that the p-value is less than 0.01.

The level of significance is taken at 0.05 or 5%. When the p-value is low, it means that the recognised values are significantly different from the population value that was hypothesised in the beginning. The p-value is said to be more significant if it is as low as possible. Also, the result would be highly significant if the p-value is very less. But, most generally, p-values smaller than 0.05 are known as significant, since getting a p-value less than 0.05 is quite a less practice.

Steps to Measure the Level of Significance

To measure the level of statistical significance of the result, the investigator first needs to calculate the p-value. It defines the probability of identifying an effect which provides that the null hypothesis is true. When the p-value is less than the level of significance (α), the null hypothesis is rejected. If the p-value so observed is not less than the significance level α, then theoretically null hypothesis is accepted. But practically, we often increase the size of the sample size and check if we reach the significance level. The general interpretation of the p-value based upon the level of significance of 10%:

  • If p > 0.1, then there will be no assumption for the null hypothesis
  • If p > 0.05 and p ≤ 0.1, it means that there will be a low assumption for the null hypothesis.
  • If p > 0.01 and p ≤ 0.05, then there must be a strong assumption about the null hypothesis.
  • If p ≤ 0.01, then a very strong assumption about the null hypothesis is indicated.

Level of Significance Example

If we obtain a p-value equal to 0.03, then it indicates that there are just 3% chances of getting a difference larger than that in our research, given that the null hypothesis exists. Now, we need to determine if this result is statistically significant enough.

We know that if the chances are 5% or less than that, then the null hypothesis is true, and we will tend to reject the null hypothesis and accept the alternative hypothesis. Here, in this case, the chances are 0.03, i.e. 3% (less than 5%), which eventually means that we will eliminate our null hypothesis and will accept an alternative hypothesis.

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