When a result is identified as being statistically significant, this means that you are confident that there is a real difference or relationship between two variables, and it’s unlikely that it’s a one-off occurrence.
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A result of an experiment is said to have statistical significance, or be statistically significant, if it is likely not caused by chance for a given statistical significance level. Your statistical significance level reflects your risk tolerance and confidence level.
Terms in this set (23) Statistical Significance. An observed difference between two 'descriptive statistics' (e.g. mean scores of two groups) that is unlikely to have occurred by chance (p. 193. Probability level/ significance level/ P-value. .05.
What does it mean when a test is statistically significant? it has reached alpha level status. the test statistic had a P-value higher than the alpha level. the test statistic had a P-value lower than the alpha level. it is important in practical terms.
This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).
When reading about or conducting research, you are likely to come across the term ‘statistical significance’. ‘Significance’ generally refers to something having particular importance – but in research, ‘significance’ has a very different meaning.
A hypothesis is a particular type of prediction for what the outcomes of research will be, and comes in two forms.
When dealing with chance, there is always the possibility of error – including Type I or Type II errors. A Type I error occurs when the null hypothesis is rejected when it should have been retained (i.e., a false positive). This means that the results are identified as significant when they actually occurred by chance.
Prior to any statistical analyses, it is important to determine what you will consider the definition of statistically significant to be.
Your hypotheses will determine which type of significance test you will need to conduct. A one-tailed hypothesis is where you predict a specific direction of the difference (higher, lower) or relationship (positive, negative) between the two groups or variables of interest.
Statistical power refers to the probability that the statistical test you are using will correctly reject a false null hypothesis. Type II errors are reduced by having enough statistical power, which is generally kept at 80% or higher. Statistical power is increased by having an adequate sample size.
Determine your thresholds and tailed tests: Before performing any analyses, decide what your alpha value is (.05 or .01), and whether you are performing a one-tailed or two-tailed test.