FAQ

In order to find a solution to a problem, the data that are gathered need to be grouped and are presented through diagrams. the data comprehensive understanding of the problem is achieved for a common man through this procedure.

Yes , probability for tossing a coin or a dice ,we may extend the phenomenon of probability to animal science research results as well.

Yes, Averages variances etc. describe the whole mass of data & a researcher can understand the data in a better way.

A hypothesis about a population is thought of as a first line statement then this hypothesis is tested though collection of data from a simple (&) of the population Example:-(A) Hypothesis: Average milk production of buffaloes in Odisha is 5 liters/day. (B) Collect data on daily milk production of 30 buffaloes across Odisha- A sample. (C) Test this Hypothesis. (D) Present the inference.

Yes different survey methods are necessary for collection of un biased data.

No, quantitative data need a different type of analysis as comparative to qualitative data Achi-square test is most appropriate for a problem relating to acceptance of poultry to farmers where the data are collected In forms of 0,1,2, or at the best up to 4 ( a quantitative data) but body weight , body length etc . needs t-test, f- test etc.

No, only the principal components & (the important characters that are un correlated) needs to be analyzed.

For a particular set of data a particular type of statical design is need to proceed.

CRD is basically a data with treatments and replications. RBD is Treatment ,replication &blocks. LSD is Treatment ,replication blocks&4th factor. So, CRD is internal Form of design, where as others are analysis for more complicated data.

No, first we have to see, whether the data a conforms to the assumptions . If not then we have to go for transformation of data & then proceed for analysis otherwise the inferences got though analysis will be a biased one.

Yes, Due to peculiar nature of unequal replications, a different type of design is required. Here Variance, heritability, repeatability etc. are important.

If we want to analyse several factors simultaneously then we need a factorial design In CRD only one factor is tested suppose we want to test effect of a drug , effect of age effect of temperature an effect of feed on milk production we have to carry out 4CRD but a single factorial design will test all these factors simultaneously.

Yes, difference effects on data can easily be calculated through matrix algebra and of course though use of computers.

Yes, this analysis find what are direct effects of some components on data & what are causing the data vary in indirect way thus cause and effect on data is established through path analysis.

D2 analysis facilitates grouping of different genotypes based on similarities through use of data on multiple characters.