Descriptive vs inferential statistics

Descriptive statistics definition is different with inferential statistics. Descriptive statistics only describes condition of the data through parameters such as mean, median, mode, frequency distribution and other statistical measurements. While inferential statistics conclude hypotheses based on sample data into population conclusion. In the descriptive statistics, we need to present:

1. Central tendency. Central tendency measurement most used is frequency distribution. These statistical measures are suitable for nominal and ordinal data (categorical data). While the mean is a measurement of central tendency for continuous data. Other descriptive measurement for central tendency is median (mid value) and mode (most frequent value).

2. Dispersion. Standard deviation is a dispersion measurement to represent spread of the data. It is suitable to measure diversity of numerical or continuous data. For categorical data, Range is suitable measurement.


Inferential vs Descriptive Statistics

While, inferential statistics is to conclude hypothesis based on the data samples into more general conclusions as whole population. Inferential research is needed if the researcher has limited research budget more efficiently so as to research done by taking several of samples less than the whole population. In the inferential study, conducted prediction. Inferential statistics requires the fulfillment of assumptions. The first assumption must be met is randomization process in sampling. This is necessary because the inferential statistics need representative population. Other assumptions that need to be met is depend on the analysis tools used. In the multiple regression analysis, the assumptions must meet multicollinearity, heteroscedasticity, autocorrelation and normality.

Statistical analysis methods used in inferential statistics are T-test, ANOVA, Anacova, regression analysis, path analysis, Structural equation modeling (SEM) and other analysis methods depending on the purpose of research. In inferential statistics, we examine hypothesis to determine whether a statistical measurement represent broader conclusions in the population. Measurement such statistics will compared to the population distribution pattern as the norm. Therefore, knowing the pattern of sample distribution to be important in inferential statistics.

Inferential Statistics in Practice

A good example of inferential statistics is in the presidential election. Many agencies conduct quick count survey to get quickly result, therefore knowing the elected presidential more quickly. The survey agency take several polling stations called TPS  as sample of the total population. TPS sample are used to generalize the overall population. Say, taken 2,000 samples of 400,000 population. The results of 2,000 polling stations are descriptive statistics. Whereas if we take the conclusions of the 400,000 polling stations is inferensial. the strength of inferential statistics depends on sampling techniques and the randomization process. If the randomization process is done correctly, then the result able to predict the population precisely. Therefore, it can save money  and time.

In the manufacturing industry, inferential statistics are very useful. Management can determine and control how many products outside the standard or defective by taking a few samples. Imagine if the management company must check all the products just to find out the defect. Certainly will spend many time and cost. Especially if we have to check all the products are packaged. Certainly not effective and efficient. Fortunately, there are Six Sigma, one of the tools used in this regard. Six Sigma principles using inferential statistics take product samples and measuring sigma or standard deviation (a measure of diversity) of the product. The number of defective products shall not exceed the certain standards.

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Ayat HIdayat Huang

Lecturer of Statistics and Research Methodology in Jakarta, Indonesia
Ayat HIdayat Huang

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