An independent samples t test was conducted to determine whether differences exist between men and women on cultural competency scores. The samples consisted of 663 women and 650 men taken from a convenience sample of public, private, and non-profit organizations. Each participant was administered an instrument that measured his or her current levels of cultural competency. The cultural competency score ranges from 0 to 10, with higher scores indicating higher levels of cultural competency. The descriptive statistics indicate women have
To determine whether a result is statistically significant, a researcher would have to calculate a p-value, which is the probability of observing an effect given that the null hypothesis is true. The null hypothesis is rejected if the p-value is less than the significance or α level. Just because you get a low p-value and conclude a difference is statistically significant, doesn’t mean the difference will automatically be important. To declare practical significance, we need to determine whether the size of the difference is meaningful. If your data is more significance, it means
A general discussion of significance tests for relationships between two continuous variables. Factors in relationships between two variablesThe strength of the relationship:is indicated by the correlation coefficient: rbut is actually measured by the coefficient of determination: r2The significance of the relationshipis expressed in probability levels: p (e.g., significant at p =.05)This tells how unlikely a given correlation coefficient, r, will occur given no relationship in the populationNOTE! NOTE! NOTE! The smaller the p-level, the more significant the relationshipBUT! BUT! BUT! The larger the correlation, the stronger the relationship Note: If statistical significance is less than 5% or P> 0.05, it means there is not much different
Sample Problem Statement: A research paper claims a meaningful contribution to the literature based on finding statistically significant relationships between predictor and response variables. In the footnotes, you see the following statement, “given this research was exploratory in nature, traditional levels of significance to reject the null hypotheses were relaxed to the .10 level.” Post your response to the scenario in which you critically evaluate this footnote. As a reader/reviewer, what response would you provide to the authors about this footnote? Response to
A statistical test estimates how consistent an observed statistic is compared to a hypothetical population of similarly obtained statistics – known as the test, or ‘null’ distribution. The further the observed statistic diverges from that test population’s median the less compatible it is with that population, and the less probable it is that such a divergent statistic would be obtained by simple chance. That compatibility is quantified as a P-value – where a low P-value indicates your observed statistic is an extreme quantile of the distribution it being
What makes significance testing a fascinating and important case for investigation is that it appears to have dispersed not because of its appropriateness in various research circumstances, but notwithstanding of it. It may certainly be the case – and I can empirically examine that an increase in the use of probability sampling refreshed the application of statistical significance testing; and inclinations in sample magnitude were linked to the approval of the .05 alpha level (Bootheway, 2014). CIs, which provide a verge of error around
