Introduction
The use of statistics is psychology is important since it helps in understanding ideas, concepts, categories, attitudes and symbols, which are normally difficult to observe and difficult to measure. This allows psychologist to test hypotheses, support theories, evaluate treatments or refute evidence. By testing hypotheses, psychologists can be able to develop new theories and come up with new innovating ideas.
Functions of Statistics
Simplification of complex problems or situations
The use of statistics simplifies the complexity of data. Raw data used in statistics or research work is normally incomprehensible. Using different statistical measures data can be made simpler. This may involve use of graphs, kurtosis, regression and correlation. For instance, the marks of two schools concerning a particular subject with a high number of students may be made comprehensible by using averages or graphs.
Statistics is used to measure results
Statistical methods are applied in the measuring of the results of educational policies, programs or technique of teachings. This is especially the case when a new approach or technique is used to replace a former method. The performance of the new approach is best determined by the statistical analysis of comparison of the two methods.
Statistics is used to compare simplified data and measure their relationship
In order to understand the meaning of an estimate of a data one needs to make a comparison with another set of data. This is normally applied in correlation. Comparison is applied in statistics to draw up conclusions. From the conclusions, researchers can be able to come up with reasons that support the difference in changes between the past and present data and what these changes might mean for future implications. Consequently, statistics can be used in forecasting trends based on current information. In cases of planners, population trends can be used to develop planning policies that will cater for an increase in population.
Formulation of Policies
Statistics is applied in the formulation of plans and policies in a number of different fields. Hence, economists, scientists and planners can apply it.
Reinforcing arguments
Statistical measures help individuals in the different fields of study to make arguments and estimates that are more accurate than with normal guessing.
Descriptive and Inferential statistics
Descriptive Statistics
Researchers to describe the properties of a particular data set use descriptive statistics (Haslam and McGarty, 2003). Descriptive statistics normally converts and describes data in tabular and graphical form. One of the common methods applied in descriptive statistics involves the use of measures of central tendency. The ‘mean’ forms a common measure of central tendency used in descriptive statistics. The mean represents the average score and is normally calculated by adding up all the different scores and then dividing the total by the number of scores (Haslam and McGarty, 2003). Another measure of central tendency is the median, which represents the most central score in a data set where the scores are arranged in ascending order. In cases where the median are two, their averages are obtained to get the median score. The mode is also a measure of central tendency and it indicates the most repetitive score in a data set. Measures of dispersion also fall under descriptive statistics. Through this, the distributive properties of the data can be established.
Inferential Statistics
Inferential statistics begin with descriptive statistics as a first step and goes further to analyze the data. According to Asadoorian and Kantarelis (2005), inferential statistics utilizes probabilistic techniques to analyze sample information from a certain known part of a population for purposes of increasing our knowledge about the population. Inferential statistics are important to researchers as they allow the effective study of a wide range of phenomena without having to conduct a census. Thus, the foundation of inferential statistics rests on the ability to make decisions about parameters (mean, variance and standard deviation) without having to complete a census of a population (Black, 2012).
Example of the relationship between descriptive and inferential statistics
Statistics involves both the use of descriptive statistics and inferential statistics where the descriptive statistics is the starting point, which then provides a baseline for using inferential statistics. Descriptive statistics is normally applied in a situation where all the data from the required population is available. For instance, in analyzing the data for biology marks in school in a state that has 1000 students, data collected for the 1000 students would be indicative of the whole population. Descriptive statistics can be applied to this data and the population parameters such as mean and standard deviation can be established. Inferential statistics comes about when analyzing a large population where obtaining all the data is not feasible. For instance, when analyzing the biology marks for a whole country. In this case, the 1000 students can be used as a representative of the whole population in that country. This representative portion is referred to as a sample. The properties of the sample are referred to as inferential statistics. Therefore, researchers will use this inferential statistics to generalize about the population. This then requires the sampling procedure to be conducted accurately to obtain a representative sample of the population.
References
Asadoorian, M. O., & Kantarelis, D. (2005). Essentials of inferential statistics. Lanham: University Press of America.
Black, K. (2012). Business statistics: For contemporary decision making. Hoboken, NJ: Wiley.
Haslam, S. A., & McGarty, C. (2003). Research methods and statistics in psychology. London: SAGE.
