#### The purpose of this assignment is to provide students with practice in identifying, reading, and critiquing systematic research reviews (SRR) related to nursing.

A systematic review is defined as A summary of evidence, typically conducted by an expert or expert panel on a particular topic, that uses a rigorous process (to minimize bias) for identifying, appraising, and synthesizing studies to answer a specific clinical question and draw conclusions about the data gathered (Melnyk & Fineout-Overholt, 2011, p. 582).

You are to use the article attach to this page and critique the last 7 question below. The article is a systemic research review (SRR). Here is some explanation to help with the critique of this articles.

Frequency counts and frequency tables could be developed on all variables but are not as useful as means, modes, and medians when describing continuous variables, such as age or blood pressure. These measures of central tendency help the consumer of research understand the patterns in the data.

The mean of a distribution is simply the average. The standard deviation, a measure of the variation in scores, represents the average amount of deviation of scores or values from the mean. Both the mean and the standard deviation should be reported in statistical results, but the range of scores is also useful in giving the consumer or researcher information regarding the distribution of scores. The mode of a distribution is the most common score. The median of a distribution is the midpoint of a distribution, the point in the distribution where 50% of the scores lie below and 50% of the scores lie above.

Calculating probabilities is dependent primarily upon the mean and standard deviation of a collection of scores, which is the probability distribution. The probability of a specific outcome is the proportion of times that the outcome would occur most of the time in repeated interval observations. A simple example is tossing a coin 10 times. The probability is that the outcome results in heads four times and tails six times. However, if the coin is tossed 100 times, the probability of the outcome being heads will be 50% of the time and tails the other 50% of the time. This is considered the law of large numbers.

The probability distribution of a continuous variable (in the above example, coins tossed) lists the possible outcomes together with their probabilities. A probability distribution has parameters describing the central tendency (mean) and variability (standard deviation). When the values for a continuous variable are graphed, a normal probability distribution is the result. The properties and characteristics of the normal probability distribution are the following.

Bell-shaped and symmetrical

The empirical rule for normal distribution consists of the following:

68.2% of the population measurements lie within one standard deviation of the mean.

95.4% of the population measurements lie with two standard deviations of the mean.

99.7% of the population measurements lie within three standard deviations of the mean

Although descriptive statistics are very useful because they show the structure and shape of the findings from a research study and they illustrate any trends over time or differences among groups, these statistics only describe the sample. By themselves, descriptive statistics are only an estimate of a possible data point in the population; they do not give an indication of how likely that point estimation reflects the true value in the population.

In an effort to indicate how likely a point estimate like a mean value is, interval estimation can be used by constructing a confidence interval (CI) around a point estimate. A range is calculated around a mean value or odds ratio. The two most common CIs are 95% and 99%. A 95% CI means that out of 100 repetitions of a study, the true value in the population would be in the middle 95% of the distribution. A 99% CI means that out of 100 repetitions of a study, the true value in the population would be in the middle 99% of the distribution.

Another aspect of interpreting the confidence interval is that the wider the interval, the less useful the point estimate is because the point estimate is less precise. In addition, if the CI for a mean difference between groups contains zero (0), then the results will probably not have statistical significance (the null hypothesis of no difference would be true if the mean difference was zero). If the interval for an odds ratio contains one (1), then the results will probably not be statistically significant (the null hypothesis of no difference would be true if an odds ratio was one [either event is equally likely]).

Inferential statistics are used to determine how confident we can be that the descriptive statistics obtained from the sample can be inferred to the population. It usually is not practical to study an entire population. As a result, inferential statistical tests were developed to determine the probability that the findings from the sample in a study can be inferred to the population. In other words, inferential statistical tests determine whether the same differences or similarities in descriptive statistics obtained from the sample would be found in the population if the entire population were studied. Thus, inferential statistics help us infer from the sample to the population.