Statistics in psychology is a branch of mathematics focused on the collection and analysis of data involving the science of mind and behavior.

1. Dr Vandana Gaur
Associate Professor
Department of Psychology

2. Definition of statistics
 Statistical analysis in psychology involves collecting
and analyzing data to discover patterns and trends.
 According to Merriam-Webster dictionary, statistics
is defined as “classified facts representing the
conditions of a people in a state – especially the
facts that can be stated in numbers or any other
tabular or classified arrangement”.
 According to statistician Sir Arthur Lyon
Bowley, statistics is defined as “Numerical
statements of facts in any department of inquiry
placed in relation to each other”.

3. Some of the important functions of statistics.
 Presents facts in the simple form
 Reduces the Complexity of data.
 Facilitates comparison.
 Testing hypothesis
 Formulation of Policies
 Formulation of Policies

4. The Importance of Statistics in Psychology
 Organize data: Visual displays such as graphs, pie
charts, frequency distributions, and scatterplots
provide researchers with a better overview of the
information, making it easier to find patterns they
might otherwise miss.
 Describe data: Descriptive statistics provide a way
to summarize data such as the number of adults
versus children or the percentage of the population
that is currently employed.
 Make inferences based on data: By using what's
known as inferential statistics, researchers can
draw conclusions about a given sample or
population.

5. TYPE OF STATISTICS
1- Descriptive Statistics
2- Inferential Statistics
 Descriptive Statistics
Descriptive statistics mostly focus on the central
tendency, variability, and distribution of sample
data. Central tendency means the estimate of the
characteristics, a typical element of a sample or
population. It includes descriptive statistics such
as mean, median, and mode.

6.  Inferential Statistics
 Inferential statistics are tools that statisticians use
to draw conclusions about the characteristics of a
population, drawn from the characteristics of a
sample, and to determine how certain they can be
of the reliability of those conclusions
 Inferential statistics are used to make
generalizations about large groups, such as
estimating average demand for a product by
surveying a sample of consumers' buying habits or
attempting to predict future events. This might
mean projecting the future return of a security or
asset class based on returns in a sample period.

8.  What is Discrete Data?
 Data that can only take on certain values are discrete
data. These values do not have to be complete
numbers, but they are values that are fixed. It only
contains finite values, the subdivision of which is not
possible. It includes only those values which are
separate and can only be counted in whole numbers or
integers, which means that the data can not be split
into fractions or decimals.

 Discrete Data Examples: The number of students in a
class, the number of chocolates in a bag, the number
of strings on the guitar, the number of fishes in the
aquarium, etc.


9.  What is Continuous Data?
 Continuous data is the data that can be of any
value. Over time, some continuous data can
change. It may take any numeric value, within a
potential value range of finite or infinite. The
continuous data can be broken down into fractions
and decimals, i.e. according to measurement
accuracy, it can be significantly subdivided into
smaller sections.

 Continuous Data Examples: Measurement of height
and weight of a student, Daily temperature
measurement of a place, Wind speed measured
daily, etc.