This document provides an introduction to statistics, covering key concepts such as: - Statistics is the science of collecting, organizing, analyzing, interpreting and presenting quantitative and qualitative data to assist decision making. - There are two main types of statistics: parametric statistics, which uses statistical assumptions and distributions, and non-parametric statistics, which does not. Descriptive and inferential statistics are the two types of parametric statistics. - Key terms are also defined, including population, sample, dataset, variables (qualitative vs. quantitative, discrete vs. continuous), and levels of measurement (nominal, ordinal, interval, ratio).

1. Introduction to Statistics
Steve Makungwa, PhD.
Forestry Department, Bunda
College, LUANAR.
Academic year: 2019/2010

2. SESSION 1: Understanding what
Statistics is…

3. What is Statistics?
It is the science of collecting, organizing, analyzing,
interpreting and presenting data to assist in making more
effective decisions.

4. Why Statistics?
1. Numerical information is everywhere… WhatsApp,
Facebook, TVs, radio, newspapers.. How are we to
determine if the conclusions reported are reasonable?
Understanding basic statistics is essential…
2. Statistical techniques are used to make decisions that
affect our personal welfare .. Insurance companies use
statistics to set rates,
3. No matter your future line of work, you will make
decisions that involve data. An understanding of
statistical methods will help you make these decisions
more effectively.

5. Types of Statistics?
Two types:
i. Parametric statistics – deals with estimating variables
using statistical assumptions or distributions
ii. Non-parametric statistics – produces estimates without
using statistical assumptions or distributions…

6. Types of Parametric Statistics?
Two types:
i. Descriptive statistics – Methods of organizing,
summarizing and presenting data in an informative way
= help to reduce our information to a manageable size
and put it into focus
ii. Inferential statistics – Methods used to find out
something about a population based on the sample

7. What is a Population & a Sample?
Population - a collection of all possible individuals, objects or
measurements of interest
Sample – a portion, subset or part of the population selected
for analysis

8. What is a Dataset?
Data set - a compiled raw information on observations using
variables for the elements of the sample, e.g. quantity of
maize produced by sampled farmers

9. What is a Variable?
Variable – is anything that can take on differing or varying
values. The values can differ at at various times for the same
object or person, or can differ at the same time for different
objects or persons
OR: A variable is a characteristic of interest concerning the
individual elements of the population or a sample; e.g. age,
education level, household size, sex, exam scores.

10. Types of variables?
Two types:
i. Qualitative variables – When the characteristic or
variable being studied is non-numeric, it is called
qualitative variable or an attribute; e.g. gender, religious
affiliation, eye color.
ii. Quantitative variables – When the variable being studied
can be reported numerically, it is quantitative variable;
e.g. number of students in a classroom, the ages of the
Malawi presidents, the weight of a bag of maize.

11. Types of Quantitative variables?
Two types:
i. Discrete variables – Quantitative variables whose can
assume only certain values, and there are usually “gaps”
between the values. Discrete variables result from
counting. Discrete variables result from counting; e.g.
No. of bedrooms in a house.
ii. Continuous variables – Quantitative variables whose
values can assume any value within a specific range.
Continuous variables result from making a measurement
of some type; e.g. weight of a packet of sugar, height of
a tree

12. Levels of measurements?
Data can be classified into levels of measurement:
i. Nominal – characterized by data that consist of names, labels,
or categories only. This cannot be arranged in an ordering
scheme and no arithmetic operations can be done on them;
e.g. religion: Christian, Muslim, Hindu.
ii. Ordinal – applies to data that can be arranged in an ordering
scheme, but differences between the data values cannot be
determined or are meaningless; e.g. student rating: poor, good,
excellent; pain level: none, low, moderate, severe.
iii. Interval – applies to data that can be arranged in some order
and for which differences in data values are meaningful –
ratios are not meaningful. This scale result from counting or
measuring; e.g. Temperature: 25oC, 28oC
iv. Ratio – applies to data that can be ranked and for which all
arithmetic operations can be performed. This scale result from
counting or measuring, and can be arranged in ordering
scheme and differences and ratios can be calculated and
interpreted; e.g. consumption: 270kg/person/year.

13. Thank you