Changes

==Qualitative data==

==Qualitative data==

Qualitative data are 'categorical' (or binary) data, and as such are often not expressed numerically. These types of data can be classified as '''nominal''' and '''ordinal''':  

Qualitative data are 'categorical' (or binary) data, and as such are often not expressed numerically, meaning that they are best summarised using percentages or proportions.These types of data can be classified as '''nominal''' and '''ordinal''':  

===Nominal===

===Nominal===

Nominal data differ from all other data types described here by lacking any order between the different categories, and can be described further as either binary ('yes/no') or categorical in nature. Examples of binary data are disease status (positive/negative), sex (male/female) and presence/absence of a factor of interest; whereas examples of categorical data are breed, coat colour, location and feed type. As there is no numerical meaning to the categories themselves, nominal data are best summarised using percentages or proportions.

Nominal data differ from all other data types described here by lacking any order between the different categories, and can be described further as either binary ('yes/no') or categorical in nature. Examples of binary data are disease status (positive/negative), sex (male/female) and presence/absence of a factor of interest; whereas examples of categorical data are breed, coat colour, location and feed type.

===Ordinal===

===Ordinal===

Ordinal data are inherently categorical in nature, but have an intrinsic order to them. Examples of ordinal data are lameness score, level of agreement with a statement (Likert items), categorised weight and categorised lactation number. As can be seen in the last two examples here, ordinal data can be created through manipulation of quantitative data. It should be noted that even if numbers are used to describe these categories, these numbers do not necessarily follow the same scale (for example, the difference between a lameness score of 5 and 3 is not necessarily the same as the difference between scores of 4 and 2). As for nominal data, ordinal data are commonly described in terms of percentages or proportions, although the median may also be used as a '''measure of central tendency'''.

Ordinal data are inherently categorical in nature, but have an intrinsic order to them. Examples of ordinal data are lameness score, level of agreement with a statement (Likert items), categorised weight and categorised lactation number. As can be seen in the last two examples here, ordinal data can be created through manipulation of quantitative data. It should be noted that even if numbers are used to describe these categories, these numbers do not necessarily follow the same scale (for example, the difference between a lameness score of 5 and 3 is not necessarily the same as the difference between scores of 4 and 2). Although ordinal data are commonly described in terms of percentages or proportions, the median may also be used as a '''measure of central tendency'''.

==Quantitative data==

==Quantitative data==

Quantitative data are numerical in nature, with a set, meaningful interval between different measurements. Quantitative data can be further classified as '''discrete''' or '''continuous''':

Quantitative data are numerical in nature, with a set, meaningful interval between different measurements. Depending on the shape of the distribution, they may be described using the mean and standard deviation (for normally distributed data), or the median and the range/interquartile range (for non-normally distributed data). Quantitative data can be further classified as '''discrete''' or '''continuous''':

===Discrete===

===Discrete===

Discrete data only include integer values, with decimal places having little or no meaning. 'Count' data, derived by counting the number of events or animals of interest, are a type of discrete data. Examples of discrete data are the number of infected animals within a group, the number of episodes of pathogen shedding following initial infection, the number of piglets born per year, and the number of lactations which the animal has been through.

Discrete data only include integer values, with decimal places having little or no meaning. 'Count' data, derived by counting the number of events or animals of interest, are a type of discrete data. Examples of discrete data are the number of infected animals within a group, the number of episodes of pathogen shedding following initial infection, the number of piglets born per year, and the number of lactations which the animal has been through.