## Statistical 2.41) were performing worse than participants

Statistical Significance

When discussing the results of
statistical significance in a research paper, this can be rather confusing to
some or may be even difficult to comprehend. If the research paper isn’t clear
and straight forward it could be a little confusing for the reader to follow as
well. Based off hypothetical research article that compared memory test performance
between two groups of participants. The first group in the study were of those
who consumed a caffeinated beverage before the test and those who consumed a
non-caffeinated beverage.

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From the hypothetical research
article: An independent samples t-test was conducted to examine the difference
between experimental conditions on test performance.  The results of this study indicated a
significant difference between participants who consumed the caffeinated
beverage and participants who did not. What the study showed based off the
results were that the participants in the caffeinated group (M = 7.64, SD =
2.41) were performing worse than participants in the non-caffeinated group (M =
9.81, SD = 3.16), t(97) = 2.14, p < .05.             When an author states that the results of any test that are statistically significant, they are explaining that the result is not by chance, but rather it is due to a specific cause.  In other words, this means that if the null hypothesis is true, there is a low probability of getting a result that high.  The computation of a significance test is based on a degree of error.  A researcher defines the probability of a sampling error which is found in any test which does not include the entire population.  It is important to have a large enough sample size to avoid any errors. If the sample size is too small chances are that the results of the study wont be as significant.  Researchers then use the p value to differentiate whether it falls below the significance level.  The p value falls below the significance level it is said to be statistically significant. The p-value is a role of the means and standard deviations of the data sample. It also measures how compatible the data is with the null hypothesis.  A high p value means that the data is likely a true null. While a low p value means that the data is an unlikely true null. A significant value is known to be true when the value is 0.05 or below.  When a p value is low, it suggests that the sample provides significant evidence that you are able to reject the null hypothesis.  The mean (M) is a demonstration of the data set.  The mean, or average, is a measure of central tendency.  Standard deviation (SD) is the number that is used to explain how the measurements of a group are spread from the average or the expected value.  Then the lower standard deviation implies that the majority of the numbers are close to the average. Those numbers that are a higher standard deviation simply show the numbers are more spread out. A t-test is used to find evidence that suggests a significant difference between population means, that are represented by two groups that are equal. It is an easy and more convenient way to test out the means. Above, the author is indicating that the participants in the caffeinated group performed worse by identifying the average in both groups – caffeinated group (M = 7.64, SD = 2.41, non-caffeinated group (M = 9.81, SD = 3.16) with a t value of 2.14.  This allows a significance on the sampling size to obtain a statistical significance of less the 0.05.  The results above show a significant difference between participants who consumed the caffeinated beverage and participants who did not, meaning caffeinated participants performed more poorly as compared to non-caffeinated participants.           References Field, A. (2013). Discovering statistics using IBM statistics (4th ed.). Sage Publications Inc. Rosnow, R. L., & Rosenthal, R. (2013). Beginning behavorial research: A conceptual primer (7th ed.). the United States of America: Pearson Education, Inc.