Running header: STATISTICAL SIGNIFICANCE AND MEANINGFULNESS 1

STATISTICAL SIGNIFICANCE AND MEANINGFULNESS 2

Statistical Significance and Meaningfulness

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Statistical Significance and Meaningfulness

Statisticians and researchers employ the statistical significance in their analysis since the surely of the event may not be assumed to be 100%. There are always a margin of a mild error that happens reducing the precision of the results obtained. This error margin usually is uncontrollable and therefore a hypothesis test is formulated to find out the significance of the results. Therefore, there is always a certain level of risk that a researcher will opt to take. This risk is what is defined as the significance level.in understating the significance levels, usually, the critical value comes into lace as both are much associated. This is because, we define whether a value is significant based on where the values lie. A significant value will lie above the critical values since its probability will be less than 0.05. In most cases, the significance level is used in hypothesis testing.

A researcher usually finds whether there is a relations that occurs in the data tested using a technique known as hypothesis testing. The test involves the use of the critical value and the P-value. Based on the choice made by the analysis, a null and an alternative hypothesis is formulated where a decision will be made. After carrying out the necessary calculations to find out if there is a statistical difference or significance of the results, the statistician will rule out a decision using the P-value to know how significant the results are. However, the P-value should be observed keenly to avoid misusing it due to some misconceptions.

The P-value in most cases is misused where it is used in the interpretation of statistical results that in one way, the results were as a result of chance. The P-value only gives the probability of the results occurring under a certain hypothetical explanation. Additionally, most nonstatistician misuse the P-value where they use it to estimate the effect size and the importance of the results obtained from the analysis. The P-value will always give a statistical significance of the results obtained and defend the spurious finding. When interpreting the P-value, we usually base our reasoning that the difference that has been obtained from the results is due to other factors apart from the explained reasons.