Statistics and statistical analysis are a branch of mathematics that cuts across many disciplines as data forms the basis for developing methodologies and analytical results to make practical and critical decisions. Through history, in almost all the subjects, it is essential to study data and check for patterns, consistencies, and inconsistencies. Therefore, the need to use data strategically to recommend the best possible interventions that give the most measurable results becomes a critical skill in society. Given the multi-disciplinary nature and practicality of statistics, the paper whose objective was to test the effectiveness of a behaviorally-based Smartphone application for weight loss combined with text messaging from a health coach on weight, body mass index, and waist circumference in young adults as compared to a control condition focused on analyzing and making an informed decision from data.
While looking at this paper, both descriptive and inferential statistics have been used while analyzing the data. Descriptive statistics is a general representation of the data which is broken into two broad categories, the measure of central tendency and the measure of variability. The measure of central tendency includes mean median and mode while the measure of variability includes standard deviation, variance, minimum, maximum, skewness, and kurtosis. For instance, the results showed that the sample was 71% female and 39% white, with an average age of 20 years and average BMI of 28.5kg/m 2 .
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Inferential and descriptive statistics both branches of statistics give information that describes the data; however, inferential statistics uses a random sample drawn from a population to make inference about the people . There are two main areas of inferential statistics, estimating parameters and hypothesis testing. Estimation of parameters takes a statistic out of a sample either the mean or the standard deviation then you make conclusions about the population mean or the population standard deviation.
Another aspect of statistics in this article is the Hypothesis Development and Testing. The hypothesis is a conjecture or specific provisional statement about the relationship between two or more variables subject to statistical test. The process of determining whether there is enough evidence to accept the conditional statement is what is referred to as statistical testing. In every research study, there are two hypotheses, the alternative hypothesis (H a ) and the null hypothesis (H 0 ). In developing, hypothesis assumption about a population parameter is made . For example, H a : The weight loss trial supports the use of Smartphone technology and feedback from a health coach on improving weight in a group of diverse young adults.
On the other hand, the null hypothesis is written with the assumption that the groups do not differ. For this case, the null hypothesis will be H 0 : The weight loss trial will not support the use of Smartphone technology and feedback from a health coach on improving weight in a group of diverse young adults. The degree to which we either accept or reject the null hypothesis is called the level of significance, often in statistics taken as 95% with an alpha of 0.05.
Test of the hypothesis is a statistical method that uses the sample data to make logical decisions of the population. For example, according to the article, the participants in the Smartphone + health coach group lost significantly more weight (p=0.026) and had a significant reduction in both BMI (p=0.024) and waist circumference (p<0.01) compared to controls. The results of this weight loss trial support the use of Smartphone technology and feedback from a health coach on improving weight in a group of diverse young adults.
