Statistical analysis

Respond to this classmates discussion post. Use sources 5 yrs old or less.

“According to Polit and Beck (2017), statistical power refers to the ability of a methodological design to ascertain the true relationship between variables. Plichta and Kelvin (2013) offer the alternative definition of power being the ability to identify statistically significant differences from a samples data. This factor is especially important in hypothesis testing since accepting or rejecting a null or alternative hypothesis is contingent on having adequate statistical power. Power is expressed in terms of probability as 1- (beta). Polit and Beck (2017) note that when the power is low, it may fail to detect the proper relationship between the independent and dependent variables. A related concept to the power of a statistical study is a Type II error, which is expressed in terms of probability as . Plichta and Kelvin (2013) define a Type II error as a false-negative conclusion whereby a researcher accepts a null hypothesis that is false. 

One of the components of power is the sample size. Power has a direct and positive relationship with sample sizes. Hence, the larger the sample size, the more the power up to the point that each additional sample results in a marginal increase in power. Similarly, type 2 errors are more likely when there is a small sample size since the analysis may fail to identify statistically significant differences from the data (Columb & Atkinson, 2016). Another component of power is the variability of the measured variables. When variables are widely distributed, then there is a high likelihood of low power or ability to detect true statistical differences; hence, leading to a type II error. Generally, having a large sample size reduces variability in the data. A final component would be the effect size or the magnitude of the relationship between variables under study (Plichta & Kelvin, 2013). When the effect size is large, then there is a lower likelihood to detect statistically significant differences from data. An effect size can be lowered by having a larger sample size.”


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