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A statistically significant result indicates that it is unlikely to be explained solely by chance or random factors. In other words, if there is no true effect in a research study, a statistically significant result has an extremely low chance of occurring.
The p-value, also known as the probability value, indicates the statistical importance of a finding. Most studies consider a p-value of 0.05 or less to be statistically significant; however, this threshold can be set higher or lower.
How do you test for statistical significance?
Data are analyze in quantitative research using null hypothesis significance testing or hypothesis testing. This is a formal process for determining the statistical significance of a relationship between variables or a difference
between groups (Arbia, 2021).
Null and alternative hypotheses
To start, research predictions are separate into two groups: null and alternative hypotheses.
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- A null hypothesis (H0) predicts that there will be no actual effect, no relationship between variables, or no difference between groups.
- An alternative hypothesis (Ha or H1) expresses your primary prediction of a true effect, a relationship between variables, or a difference between groups.
Hypothesis testing always begins with the premise that the null hypothesis is true. You can assess the likelihood (probability) of receiving your results under this assumption using this approach. You can reject or keep the null hypothesis based on the results of the test.
What is a significance level?
The significance level, or alpha, is a value determined in advance by the researcher as the threshold for statistical significance. It is the maximum risk that you are ready to take of reaching a false positive conclusion (Type I error).
In a hypothesis test, the p-value is compare to the significance level to
determine if the null hypothesis should be reject.
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- The null hypothesis is not refute if the p-value is greater than the significance level, yet the results are not statistically significant.
- If the p-value is less than the level of significance, the results are interpret as contradicting the null hypothesis and are report as statistically significant.
The significance level is usually set at 0.05 or 5%. To be statistically significant, your results must have a 5% or lower chance of occurring under the null hypothesis.
For a more conservative test, the significance level can be reduce. That is, for an effect to be statistically significant, it must be greater.
For significance testing in non-academic marketing or business contexts, the significance threshold can also be raise. This makes the study less rigorous and enhances the possibility of discovering a statistically significant result.
As a best practice, choose a significance level before starting your study. Otherwise, your results can be easily manipulate to reflect your research predictions.
It is important to keep in mind that hypothesis testing can only show you whether to reject the null hypothesis or the alternative hypothesis. It can never “prove” the null hypothesis because the lack of a statistically significant effect does not imply that no effect occurs at all.
Other types of significance in research
In addition to statistical significance, clinical and practical significance are important research outcomes.
Practical significance shows you whether the research outcome is important enough to be meaningful in the real world. It is indicate by the effect size of the study.
Clinical significance is important for intervention and treatment studies. A treatment is consider clinically significant when it improves the lives of patients in a tangible or significant way.
Problems with relying on statistical significance.
There are numerous critiques of the concept of statistical significance and its application in research.
Researchers use a traditional criterion that has no scientific or practical basis to classify results as statistically significant or non-significant. This indicates that a 0.001 decrease in a p-value can convert a research conclusion from statistically non-significant to significant with almost no visible effect change.
Statistical significance can be misleading on its own because it is modified by sample size. Very large samples are more likely to produce statistically significant results, even if the effect is modest or negligible in practice.
This means that small effects are often exaggerated if they meet the significance threshold, while interesting results are overlooked if they fall short.
Over the last few decades, the emphasis on statistical significance has resulted in a significant publication bias and replication crisis in the social sciences and medicine. Findings are normally only published in academic publications if they are statistically significant—but statistically significant outcomes are frequently not reproducible in high-quality replication studies.
As a result, many scientists argue that statistical significance should be phased out as a decision-making tool in favor of more nuanced approaches to evaluating results.
As a result, APA standards recommend reporting not only p values but also effect sizes and confidence intervals whenever possible in to show the real-world implications of a research outcome
Conclusion
Remember to always consult with your principal investigator or statistician, or software, if you are unsure about statistical significance. Even you can also Take My Statistics Class For Me or
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Reference
Arbia, G., 2021. Statistics and Empirical Knowledge. In Statistics, New Empiricism and Society in the Era of Big Data (pp. 21-44). Springer, Cham.
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