Observations recorded during research constitute data. There are three types of data i.e. nominal, ordinal, and interval data. Statistical methods for analysis mainly depend on type of data.

**Types of Data**:-

Observations
recorded during research constitute data. There are three types of data i.e.
nominal, ordinal, and interval data. Statistical methods for analysis mainly
depend on type of data.

** Nominal data: **This
is synonymous with categorical datawhere data is simply assigned “names” or
categories based on the presence or absence of certain
attributes/characteristics without any ranking between the categories. For
example,bacterial culture studies are categorized by growth as positive or
negative to particular growth media. It also includes binominal data, which
refers to two possible outcomes. For example, outcome of cancer may be death or
survival, drug therapy with drug ‘X’ will show improvement or no improvement at
all.

**Ordinal data**:
It is also called as ordered, categorical, orgraded data. Generally, this type
of data is expressed as scores or ranks. There is a natural order among
categories, and they can be ranked or arranged in order. For example, speed may
be classified as slow, medium, and fast. Since there is an order between the
three grades of speed, this type of data is called as ordinal. To indicate the
intensity of speed, it may also be expressed as scores (slow = 1, medium = 2,
fast = 3). Hence, data can be arranged in an order and rank.

**Interval data**:
This type of data is characterized by an equaland definite interval between two
measurements. For example, weight is expressed as 20, 21, 22, 23, 24 kg. The
interval between 20 and 21 is same as that between 23 and 24. Interval type of
data can be either continuous or discrete. A continuous variable can take any
value within a given range. For example: hemoglobin (Hb) level may be taken as
11.3, 12.6, 13.4 gm % while a discrete variable is usually assigned integer
values i.e. does not have fractional values. For example, number of meals per
day by a person is generally discrete variables. Sometimes, certain data may be
converted from one form to another form to reduce skewness and make it to
follow the normal distribution. For example, plant growth are converted to
their log values and plotted in growth response curve to obtain a straight line
so that analysis becomes easy. Data can be transformed by taking the logarithm,
square root, or reciprocal. Logarithmic conversion is the most common data
transformation used in agricultural research.

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