Before starting the assignment help and homework help sections, students are required to have a good understanding of statistical significance. We , Courseworktutors having years of experience in statistical significance Assignment help below information will equip the students in dealing with the assignment help and homework help sections for this topic.
An Introduction to statistical significance
Significance as a statistical term, allows us to determine how sure you are that a relationship exists. In order to say that a significant difference or a relationship exists simply points to half the story. Even if there is an observation which confirms the relationship exists, it does not certify the intensity of the relationship. It could be a strong, moderate, or weak relationship. After finding a significant relationship, its strength has to evaluated. Significant relationships can either be strong or weak. Significant differences can be small or large. However, the significance is dependent on the sample size.
Hypothesis testing and statistical significance
Statistical significance plays a central role in hypothesis testing. It is used to decide on whether the null hypothesis should be retained or rejected. The null hypothesis states the default assumption. It states that there is no change. In order to reject the null hypothesis, an observed outcome has to be statistically significant. In other words, the observed p-value should be less than the predetermined value of the significance level.
Statistical significance and determining test results
To determine if a result has statistical significance, we use the p-value. It is the probability to observe an effect when the null hypothesis is true. The null hypothesis is rejected, if the p-value is less than a specified level, denoted by α. α is also called as the significance level. This shows the probability of rejecting the null hypothesis when it is true. This is referred to as a Type 1 error. Generally, it is set at or below 5%.
If we say that when α is set to 5%, the conditional probability that a type I error occurs is 5%. A statistically significant result is one in which the observed p-value falls less than 5%.
The rejection region comprises 5% of the distribution.This 5% can either be allocated to only one side of the sampling distribution. This called as a one-tailed test. On the other hand, it can be partitioned to both sides of the distribution. This is referred to as a two-tailed test. In a two-tailed test, each tail contains 2.5% of the distribution.
Statistical Significance in One-Tailed and Two-Tailed Hypothesis Test
The use of a one-tailed test is based on whether alternative hypothesis specifies a direction. Whereas, a two-tailed test can be used for the same hypotheses. But, in comparison, it will be a less powerful than a one-tailed test. This can be attributed to the fact that the rejection region for a one-tailed test is concentrated on one end of the null distribution. Moreover, it is twice the size of a two-tailed test (5% vs. 2.5%). However, a one-tailed test is only more effective than a two-tailed test only if the specified direction of the alternative hypothesis is correct. On the contrary, If the direction is wrong, then the one-tailed test is not effective.
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