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It is quantitatively degree of variation or dispersion of values in a population or in a sample. These are indices that give us an idea about homogeneity or heterogeneity of the data. Variance, SD, standard error, quartile, interquartile range, percentile, range, and CV.

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The SD is a measure of how spread out values is from its mean value. It is called SD because we have taken a standard value mean to measures the dispersion. The variance s 2 is defined as the average of the squared difference from the mean.

It is equal to the square of the SD s. For example, in the above, SD is Similarly, variance is Standard error is the approximate difference between sample mean and population mean. When we draw the many samples from same population with same sample size through random sampling technique, then SD among the sample means is called standard error.

If sample SD and sample size are given, we can calculate standard error for this sample, by using the formula.

For example, according to Table 2 , standard error is 2. The quartiles are the three points that divide the data set into four equal groups, each group comprising a quarter of the data, for a set of data values which are arranged in either ascending or descending order. Q1, Q2, and Q3 are represent the first, second, and third quartile's value. For example, in the above example, three quartiles, that is, Q1, Q2, and Q3 are 88, 95, and , respectively.

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As the first and third quartile in the data is 88 and Interpretation of SD without considering the magnitude of mean of the sample or population may be misleading. To overcome this problem, CV gives an idea. For example, in the above, coefficient of the variation is Difference between largest and smallest observation is called range. For example, in the above, minimum and maximum observation in the data is 82 mmHg and mmHg. Hence, the range of the data is 34 mmHg also can write like: 82— [ Table 2 ]. The standard normal distribution is the most important continuous probability distribution has a bell-shaped density curve described by its mean and SD and extreme values in the data set have no significant impact on the mean value.

If a continuous data is follow normal distribution then Various statistical methods used for data analysis make assumptions about normality, including correlation, regression, t -tests, and analysis of variance. Central limit theorem states that when sample size has or more observations, violation of the normality is not a major issue. If a continuous data follow normal distribution, then we present this data in mean value.

If our data are not normally distributed, resultant mean is not a representative value of our data. A wrong selection of the representative value of a data set and further calculated significance level using this representative value might give wrong interpretation. If applicable, then means are compared using parametric test otherwise medians are used to compare the groups, using nonparametric methods. An assessment of the normality of data is a prerequisite for many statistical tests because normal data is an underlying assumption in parametric testing.

There are two main methods of assessing normality: Graphical and numerical including statistical tests. Graphical interpretation has the advantage of allowing good judgment to assess normality in situations when numerical tests might be over or undersensitive. Although normality assessment using graphical methods need a great deal of the experience to avoid the wrong interpretations.

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If we do not have a good experience, it is the best to rely on the numerical methods. The two well-known tests of normality, namely, the Kolmogorov—Smirnov test and the Shapiro—Wilk test are most widely used methods to test the normality of the data. For both of the above tests, null hypothesis states that data are taken from normal distributed population. Skewness is a measure of symmetry, or more precisely, the lack of symmetry of the normal distribution. Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis proper.

A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. Although this is a less reliable method in the small-to-moderate sample size i. To overcome this problem, a z -test is applied for normality test using skewness and kurtosis. A Z score could be obtained by dividing the skewness values or excess kurtosis value by their standard errors. If the graph is approximately bell-shaped and symmetric about the mean, we can assume normally distributed data[ 12 , 13 ] [ Figure 1 ]. In statistics, a Q—Q plot is a scatterplot created by plotting two sets of quantiles observed and expected against one another.

For normally distributed data, observed data are approximate to the expected data, that is, they are statistically equal [ Figure 2 ]. A P—P plot probability—probability plot or percent—percent plot is a graphical technique for assessing how closely two data sets observed and expected agree. It forms an approximate straight line when data are normally distributed.

Departures from this straight line indicate departures from normality [ Figure 3 ]. Box plot is another way to assess the normality of the data. It shows the median as a horizontal line inside the box and the IQR range between the first and third quartile as the length of the box. The whiskers line extending from the top and bottom of the box represent the minimum and maximum values when they are within 1. A box plot that is symmetric with the median line at approximately the center of the box and with symmetric whiskers indicate that the data may have come from a normal distribution.

In case many outliers are present in our data set, either outliers are need to remove or data should treat as nonnormally distributed[ 8 , 13 , 14 ] [ Figure 4 ].

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Another method of normality of the data is relative value of the SD with respect to mean. If SD is less than half mean i. However this method should only be used when our sample size is at least Normal Q—Q Plot showing correlation between observed and expected values of the mean arterial pressure. Normal P—P Plot showing correlation between observed and expected cumulative probability of the mean arterial pressure.

For example in Table 1 , data of MAP of the 15 patients are given.

Normality of the above data was assessed. The study is designed to determine how Aboriginal and Torres Strait Islander children develop resilience, what helps to improve their wellbeing and future, and to recognise links between early childhood experiences and later life outcomes. The Longitudinal Study of Humanitarian Migrants BNLA is a longitudinal study of the settlement experience of humanitarian arrivals in Australia, over five years, from until The project aims to increase understanding of the well-being and experiences of humanitarian migrants settling in Australia.

More than 1, migrating units 2, individuals were interviewed in the first stage of the study. The study will follow individuals and migrating units through their settlement journey and look at their experiences, challenges, adaptations and outcomes over time. Eurostat provides access to 12 listed below datasets. View detailed information about the organisation and availability of the datasets. You can use Print Screen key on your keyboard and paste the picture into the email. Stats PC is entered on the LibCal Room Booking software as one of the study spaces managed by the Library and with the same conditions of use.

This OST could be used free of charge by staff and students. The university does not offer technical support in setting up the surveys in any OST. This is also the case with Qualtrics who offers various webinars and other educational resources. Questions about psychometric and research aspects of designing your questionnaire can be addressed by the university statistical consultant. Furthermore, a number of OST related publications are available in the library, to mention but a few:. Research Data Australia is a very extensive government sponsored tool to find, access, and reuse data for research.