This review throws the light on applications of statistics in animal sciences, and answering the question of how are statistics playing a vital role in veterinary field and biology .Also it presents different statistical methods that can be used in different studies. The basic statistical concepts should be known. The subject of statistics includes, design of a study that it will provide the biologist with the most information efficiently, Collection of the data by using different sampling methods, and analysis of the data using different statistical tests. It is not necessary to understand the exact methodology of every statistical test used, but it is necessary to understand …show more content…
They are records of measurement, counts or observations. Examples of data are body weights of calves .
There are four types of data: nominal, ordinal, interval and ratio. Qualitative variables are measured with nominal and ordinal, while quantitative variables are measured with interval and ratio scale of measurement .The type of data is an important factor in determining which computations are suitable.
Data collected and organized by the experimenters would be described by measures of central tendency and measures of dispersion. In scientific and technical literature, experimental data is often summarized either using the mean and standard deviation or the mean with the standard error.
It is the most common measure of central tendency. It is a summary measure for a quantitative variable. It is calculated by summing all the observations in a set of data and then dividing the total by the number of values involved. It is very sensitive to outliers. x^-=(∑▒x)/n Standard …show more content…
It is suitable for either numerical or categorical data (Table 2). It compares the means of 2 groups to determine if they are the same or not. It is two types (paired and independent t test).
Analysis of variance
F test is a generalization of the t test (or Wilcoxon or Mann-Whitney U test) when 3 or more groups are being compared. There are both parametric and nonparametric analyses of variance referred to as ANOVA by sum of squares or ANOVA by rank, respectively (Figure 2).
Pearson product moment correlation coefficient
It can be used to measure the intensity of association between variables. The data must be normally distributed. It defines both the strength and direction of the linear relationship between two variables. It does not imply causation.
Regression quantifies the numerical relationship between 2 variables that are correlated. Regression is better suited for studying functional dependencies between factors. It creates an equation where in if one variable is known, the other can be estimated.
No assumptions are made about the data distribution. Ordinal properties of data are used instead of absolute data values. Statistical significance with a nonparametric test is difficult to be