Sustainable development has many definitions; one of them is, “development that satisfies the needs of the present without compromising the capacity of future generations”. Sustainability is three dimensioned; Economic, Socio-cultural and Environment. These dimensions are measured by various mathematical/ statistical indicators. Yet the accuracy and reliability of these indicators are debatable. For example; economic sustainability of a country is measured by its Gross Domestic Product (GDP). The GDP is defined as the total monetary or market value of all the finished goods and services produced within a country in a specific time period. This indicator has key omissions; one is an unpaid work in home and volunteering. Women do a majority of this unpaid work all over the world. This issue has been in debate over the decades and almost come to a conclusion that “home production” need to be included in GDP calculation. Yet it has not become practical. According to U. S. Bureau of Economic Analysis; “lack of reliable data influenced the decision to leave household production out of GDP”. In other words, this is due to lack of knowledge or applications of “Statistics” for the purpose.Statistics, known as “Mathematics of uncertainty”, involves in developing and studying methods for; collecting, analyzing, evaluating, interpreting and presenting information. Gender statistics is needed to measure and monitor the equalities and inequalities in the situation of women and men in all areas of life. It helps policymakers to formulate and monitor policies and plans. Statistics has two main parts; Descriptive Statistics and Inferential Statistics. In descriptive statistics, sample data are analyzed by measures of location and measures of dispersion. This part of the analysis leads to Inferential Statistics, which generalizes statistical findings to population. For example, unemployment data of male and female of a sample could be understood by mean and standard deviation. If the unemployment of females is higher than males in the sample, it is erroneous to conclude the same for population, until the mean comparison confirm the result. However, most important part of a study is not data analysis, but is data collection. If the data has not come from a random sample, then the descriptive statistics become biased estimates for population parameters; probability estimates become unrealistic. Statistical analysis tools comprise various statistical models; most popular ones are Regression Models and Time Series Models. Regression models are useful in tracing causal relationships; that is to estimate the monthly income of a person based on; gender, age, level of education etc. Time Series models can be used to forecast gender statistics. If gender data; income, literacy, unemployment, life expectancy etc are tested on time series models and fitted, they will be extremely helpful in overcoming gender issues. The Economic and Social Council of United Nations emphasized the importance of; reliable, timely and meaningful gender statistics in achieving millennium development goals. But many countries have never done a proper survey on gender statistics. Also statistical techniques are not properly used in achieving sustainability goals. Instead they are being misused. According to world Statisticians, misuse can be a result of; negligence, purposive or mainly due to lack of knowledge. Hence it is essential to gain knowledge, apply theme and improve them in achieving our objectives
