# Response to training

In this study it is reported that maximal o2 uptake increased from 39.2_+7.7 to 46.9_+8.1 ml.Kg-1.min-1 in response to training (P = 0.05)

1. The numbers above are mean _+ standard deviation (for before and after training). What is standard deviation and why is it a useful value to report?

Standard deviation is a really difficult term to discuss. Mainly, it is because of the fact that it is not a job of normal person to deal with this statistical term. However, standard deviation is really important especially to those that are ignorant of the term. Standard deviation is a statistic that denotes intactness of a data set. This means that it is a value that determines how far a value is from the mean. The mean is the average of all the data. It is obtained by adding all the data and dividing it by the total number of data in a data set. The smaller the standard deviation, the closer the data will be to the mean and the larger the standard deviation, the further away the data would be from the mean. If a given data is reported by the use of distribution curves, one can determine if the

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2. What is the meaning of the P value and why is it useful to report?

To figure out what P is, you will need to understand terms like null hypothesis (Ho), alternative hypothesis (HA), Type I error, and alpha (?).

Include in your answer the general meaning of these terms as well as how they apply to the data referred to above.

In a statistical analysis, the P-value is one of the most important data to be obtained and remembered. P in P-value stands for probability. But what probability does this P-value describe? The P-value is related to confidence intervals and hypothesis testing. The P-value is the value in which it is compared to the level of significance in order to determine whether an analysis is significant or not. The level of significance is a value in which a person is able to reject a null hypothesis. In other words, the level of significance is the level of rejection. A level significance is denoted as alpha (?). A null hypothesis is the statement that tells us if a test has made no effects to the analysis. In case the null hypothesis is rejected, the alternative hypothesis is accepted. The alternative hypothesis is a statement that tells us that a test has made a significant effect to the analysis. That is if P-value is less than the level of significance then one can reject the null hypothesis, accept the alternative hypothesis and can say that the analysis is significant and if the P-value is greater than the level of significance then one is unable to reject the null hypothesis and unable to accept the alternative hypothesis and can say that the analysis is insignificant and needs further testing. If one is unable to correctly use a P-value, certain errors might occur. One error that can be encountered is the type I error. This error is related to the alpha or the level of significance. This error is made if one rejects the null hypothesis when in fact the null hypothesis is true (Hopkins, 2009).

Referring to the problem above, we can determine if the data analysis is significant or not by establishing an alpha or level of significance and hypothesis. The null hypothesis is that the training made no effect to the maximal oxygen intake. The alternative hypothesis will be that the training has an increase effect to the maximal oxygen intake. If we establish the level of significance to be 0.1 (? = 0.1) then our decision will be to reject the null hypothesis and accept the alternative hypothesis since the P-value which is 0.05 is less than the alpha value which is 0.1. Thus, we can now say that we are 90% confident that the training has an increase effect to the maximal oxygen intake.

References

Hopkins, W. (2009). A New View of Statistics. Retrieved April 30, 2009 from http://www.sportsci.org/resource/stats/pvalues.html

Niles, R. (2009). Standard Deviation. Retrieved April 30, 2009 from http://www.robertniles.com/stats/stdev.shtml