# Primary Data vs Secondary Data – Statistics (Similarities and Differences between Primary Data and Secondary Data)

The data is a set of values of qualitative or quantitative variables. In statistics, the data are the individual observations. The scientific investigations involve observations on variables. The observations made on these variables are obtained in the form of ‘data’.

Based on the source, the data is categorized into TWO types: (1) Primary Data and (2) Secondary Data.

Primary Data: Data collected for the first time by the original investigator.

Secondary Data: The data used in statistical investigations which have already been collected by some other for their purpose and published.

The present post discusses the Differences between Primary Data and Secondary Data with a Comparison Table.

# Types of Experimental Designs in Statistics Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD) – Advantages and Disadvantages

In the previous post, we have discussed the Principles of Experimental Designs. There we discussed the concept of Experimental design in statistics and their applications. In the present post, we will discuss different types of statistical experimental designs, its applications, advantages and limitations.

## Different types of Experimental Designs

Ø  Experimental designs are broadly classified into TWO categories:

(A).   Single-Factor Experiments

(B).  Multi-factor Experiments

(A). Single-Factor Experiments:

Ø  Single factor experiments are those experiments in which only a single factor varies while all others are kept constant.

Ø  Here the treatments consist exclusively of the different levels of the single variable factor.

Ø  All other factors are applied uniformly to all plots.

Ø  Examples of Single-Factor Experimental Designs:

(1). Completely Randomized Design (CRD)

(2). Randomized Block Design (RBD)

(3). Latin-Square Design (LSD)

# Experimental Designs in Statistics (The Principles of Experimental Designs in Research Methodology)

What is a statistical experiment?

Ø  An experiment is a plan for the collection and analysis of data.

Ø  “It is a controlled act through which data are collected according to some pre-determined objective”.

Ø The observations obtained from a carefully planned and well-designed experiment in advance only gives valid inferences.

Ø An experimental design which gives the smallest error is supposed to be the best design for a particular type of investigation.

Experimental unit (Experimental Plot):

Ø  The smallest division of the experimental material to which we apply the treatment and can make the observation on it is called experimental unit or experimental plot

Treatments:

Ø  Treatments are the characteristics which are to be investigated through an experiment.

Ø  The treatments are the objects of comparisons in an experiment.

Ø  Example: effects of different fertilizers, the yield of different varieties of a crop, disease resistance of different cultivars etc.

# Statistical Hypothesis Testing (Statistical Significance Testing – Simple Easy Lecture Notes)

“Truth can be stated in a thousand different ways, yet each one can be true…”
Swami Vivekananda

## What is ‘Test of Hypothesis’?

Ø  Test of Hypothesis (Hypothesis Testing) is a process of testing of the significance regarding the parameters of the population on the basis of sample drawn from it.

Ø  Test of hypothesis is also called as ‘Test of Significance’.

Ø  J. Neyman and E.S. Pearson initiated the practice of testing of hypothesis in statistics.

What is the purpose of Hypothesis Testing?

Ø  The main purpose of hypothesis testing is to help the researcher in reaching a conclusion regarding the population by examining a sample taken from that population.

Ø  The hypothesis testing does not provide proof for the hypothesis.

Ø  The test only indicates whether the hypothesis is supported or not supported by the available data.

What is Hypothesis?

Ø  Hypothesis is a statement about one or more populations.

Ø  It is a statement about the parameters of the population about which the statement is made.

Ø  Example:

\$  A doctor hypothesized: “The drug ‘X’ is ineffective in 99% of cases of which it is used”.

\$  “The average pass percentage of central university degree programme is 98”.

Ø  Through the hypothesis testing the researcher or investigator can determine whether or not such statements are compatible with the available data.

# Statistical Data / Variables – Introduction (Classification of Statistical Data / Variable – Numeric vs Categorical)

## What is ‘data’ or ‘variable’?

Ø  Data is a set of values of qualitative or quantitative variables.

Ø  In biostatistics (also in statistics) data are the individual observations.

Ø  The scientific investigations involve observations on variables.

Ø  The observations made on these variables are obtained in the form of ‘data’.

Ø  Variable is a quantity or characteristic which can ‘vary from one individual to another’.

Ø  Example: Consider the characteristic ‘weight’ of individuals and let it be denoted by the letter ‘N’. The value of ‘N’ varies from one individual to another and thus, ‘N’ is a variable.

Ø  Data and variable are not exact but used frequently as synonyms.

Ø  The variables can also be called as ‘data items’.

Ø  Majority of the statistical analysis are done on variables.

### Type of Variables in Statistics

Statistical variables can be classified based on two criterion (I) Nature of Variables and (II) Source of variables

I. Classification of variable based on Nature of Variables

Ø  Based on the nature of variables, statistical variables can be classified to TWO major categories such as (1) Numerical and (2) Categorical.