INTRO TO STATISTICS

     

Statistics simply put is numerical data collected systematically. However a well constructed definition will be given as ‘’Statistics is the scientific collection, observation, summarizing, presentation and analyses of data, with a view of drawing conclusion and making decisions based on the analysis of such data.

NATURE OF STATISTICS

Statistics can be broadly divided into two, namely;

·          Descriptive  Statistics

·          Inferential or Analytical

DESCRIPTIVE STATISTICS involves describing large masses of data and not making inference[mean,median,mode].

INFERENTIAL STATISTICS involves drawing conclusion from observed data[it makes use of sampling].

               ANOTHER aspect deals with VARIABLE and ATTRIBUTE

     Variable refers to the characteristics been measured in a given statistical study[weight,height,distance].

       Attribute refers to what cannot be measured by numbers[beauty,love,hatred].

NB; Data can be collected, classified, presented, represented.

IN CLASSIFICATIONS OF DATA, Data can be classified into;

·          QUALITATIVE DATA

·          QUANTITATIVE DATA

Data can also be DISCREETE and CONTINOUS data.

     Discreete data is the data that takes on whole numbers[number of pupils in a class, number of cars in park].

     Continuous data on the other hand can increase continuously.[The age of individuals, the population of people, which can both increase and increase]

DEFINE THE FOLLOWING TERMS

·          POPULATION

·          SAMPLE

·          STASTIC

·          PARAMETER

Population refers to the collection of persons, places, things about which we want to obtain information.

NB; When the population is relatively small in size, each element of the population can be examined, This is called CENSUS.

However, in a normal situation, a population is to big to examine all the elements so we conduct a SAMPLE.

     SAMPLE is a fractional part of the POPULATION Taken for the purpose of an investigation.

      The process of taking a sample is called SAMPLING.

KEY CONCEPTS IN SAMPLING ARE;

 SAMPLING SURVEY, SAMPLING DESIGN, SAMPLING FRAME, SAMPLING TECHNIQUES.

   A Sample survey is useful in identifying sampling UNITS.

   A Sample Frame is list of all Sampling UNITS.

   A Sample Design is used to randomly select Sampling UNITS.

        SAMPLING TECHNIQUES are used in selecting sample from a given population. SAMPLING  TECHNIQUE includes SAMPLING FRAME.

SAMPLING TECHNIQUES can be;

·          Probability/Random Sampling.

·          Non-Probability/Random Sampling.

NB;

Sample survey can be of the following ADHOC survey, REPITITIVE survey, CONTINUOUS survey, MULTI-SUBJECT survey. E.T.C

NOW, back to  probability/random Sampling. This technique ensures that all members of a given Population or study all have ‘’equal chances of been selected’’.

Types of Probability Sampling

·          Simple random  sampling[means that every item in the population have equal chances of been selected]

·          Stratified random sampling[where the population is heterogeneous which can be divided into STRATA]

·          Cluster sampling[when the basic sampling is to be found in group of clusters]

·          Systematic sampling[when a list of a population is available, a sample of every item is called SYSTEMMATIC Sample]

·          Multistage sampling[This involves many stages and it is convenient if the criteria of sampling involves geographical and political conditions]

 NOW, back to Non Probability/Random Sampling, this is a sampling technique in which elements of a population ‘’does not have equal chance’’ of being selected. It is based on certainty. Examples include;

·          Purposive sampling; Sample is selected based on the purpose the sample or the data it is needed for.

·          Convenience sampling; sample are selected based on ease or availability to participate in study.

·          Purposive / Judgmental sampling: sample is selected based on the purpose the sample or data is needed for.

·          Others include snowball sampling, quota sampling, etc.

LETS NOW EXAMINE parameter and statistic.

       PARAMETER is a measure used to describe the population [majorly its characteristics].

       STATISTIC is a descriptive measure computed from SAMPLE data.       NB; PARAMETER is to POPULATION, STATISTIC  is to SAMPLE.

SOURCES OF DATA

  Basically sources of statistical data involves [surveys, observation, experiment and census]. However, there are two major sources of data which are;

·          PRIMARY DATA[data collected by the researcher or investigator for the purpose of a particularly study]

·          SECONDARY DATA[data collected from existing data i.e data collected by someone else other than the researcher.

METHODS OF COLLECTING PRIMARY DATA INCLUDES

·          Personal interview, telephone interview, direct observation, laboratory experiments, mail, Questionnaire E.T.C

METHODS OF COLLECTING SECONDARY DATA INCLUDES

·          Government agencies, census, journals E.T.C  

USES OF STATISTICS

·          AIDS IN ILLUSTRATING DATA

·          AIDS IN DESCRIPTION OF DATA

·          AIDS IN DECISION MAKING

·          AIDS IN FORECASTING

·          AIDS IN PLANNING .

I KNOW YOU ENJOYED READING THIS PIECE OF WORK BY THE GUIDE’S EDU CONSULTS.

ARTICLE BY KELVIN EGUAOJE (DOUBLE DEE)