Phrasing statement to conceal By Prof Dr Sohail Ansari

 Wise men speak because they have something to say; Fools because they have to say something. Plato
So verily with the hardship there is relief, verily with the hardship there is relief – Quran Ch 94:5-6

Communication is to hide than to communicate:

The main purpose of communication is to hide than to communicate; however it does not mean, as it is usually thought so, hiding absolutely or communicating false hood, it means phrasing statement so that it contains lesser than essential (required to make judgment) information.

Relationship between Variables

It is very important to understand relationship between variables to draw the right conclusion from a statistical analysis. The relationship between variables determines how the right conclusions are reached. Without an understanding of this, you can fall into many pitfalls that accompany statistical analysis and infer wrong results from your data.

 

There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else?

Suppose you measure a volume of a gas in a cylinder and measure its pressure. Now you start compressing the gas by pushing a piston all while maintaining the gas at the room temperature. The volume of gas decreases while the pressure increases. You note down different values on a graph paper.

If you take enough measurements, you can see a shape of a parabola defined by xy=constant. This is because gases follow Boyle's law that says when temperature is constant, PV = constant. Here, by taking data you are relating the pressure of the gas with its volume. Similarly, many relationships are linear in nature.

Relationships in Physical and Social Sciences

Relationships between variables need to be studied and analyzed before drawing conclusions based on it. In natural science and engineering, this is usually more straightforward as you can keep all parameters except one constant and study how this one parameter affects the result under study.

However, in social sciences, things get much more complicated because parameters may or may not be directly related. There could be a number of indirect consequences and deducing cause and effect can be challenging.

Only when the change in one variable actually causes the change in another parameter is there a causal relationship. Otherwise, it is simply a correlation. Correlation doesn't imply causation. There are ample examples and various types of fallacies in use.

A famous example to prove the point: Increased ice-cream sales shows a strong correlation to deaths by drowning. It would obviously be wrong to conclude that consuming ice-creams causes drowning. The explanation is that more ice-cream gets sold in the summer, when more people go to the beach and other water bodies and therefore increased deaths by drowning.

 

Relationships between Variables

In any experiment, the object is to gather information about some event, in order to increase one's knowledge about it. In order to design an experiment, it is necessary to know or make a proposed explanation about cause and effect relationships between what you change in the experiment and what you are measuring. In order to do this, scientists use established theories to come up with a hypothesis before experimenting.

Quantitative and Qualitative Data What are quantitative and qualitative data? Quantitative data are measures of values or counts and are expressed as numbers. Quantitative data are data about numeric variables (e.g. how many; how much; or how often). Qualitative data are measures of 'types' and may be represented by a name, symbol, or a number code. Qualitative data are data about categorical variables (e.g. what type). Quantitative = Quantity Qualitative = Quality Data collected about a numeric variable will always be quantitative and data collected about a categorical variable will always be qualitative. Therefore, you can identify the type of data, prior to collection, based on whether the variable is numeric or categorical. Why are quantitative and qualitative data important? Quantitative and qualitative data provide different outcomes, and are often used together to get a full picture of a population. For example, if data are collected on annual income (quantitative), occupation data (qualitative) could also be gathered to get more detail on the average annual income for each type of occupation. Quantitative and qualitative data can be gathered from the same data unit depending on whether the variable of interest is numerical or categorical. For example: Data unit Numeric variable = Quantitative data Categorical variable = Qualitative data A person "How many children do you have?" 4 children "In which country were your children born?" Australia "How much do you earn?" $60,000 p.a. "What is your occupation?" Photographer "How many hours do you work?" 38 hours per week "Do you work full-time or part-time?" Full-time A house "How many square metres is the house?" 200 square metres "In which city or town is the house located?" Brisbane A business "How many workers are currently employed?" 264 employees "What is the industry of the business?" Retail A farm "How many milk cows are located on the farm? 36 cows "What is the main activity of the farm?" Dairy How can you use quantitative and qualitative data? It is important to identify whether the data are quantitative or qualitative as this affects the statistics that can be produced. Frequency counts: The number of times an observation occurs (frequency) for a data item (variable) can be shown for both quantitative and qualitative data. The graphs below arrange the quantitative and qualitative data to show the frequency distribution of the data. Quantitative Data Numerical variables                The values of a numerical variable are numbers. They can be further classified into discrete and continuous variables. Discrete numerical variable A variable whose values are whole numbers (counts) is called discrete. For example, the number of items bought by a customer in a supermarket is discrete. Continuous numerical variable A variable that may contain any value within some range is called continuous. For example, the time that the customer spends in the supermarket is continuous. Statistical methods that can be used for continuous variables are not always appropriate for discrete variables. The distinction between discrete and continuous variables is important. Categorical variables The values of a categorical variable are selected from a small group of categories. Examples are gender (male or female) and marital status (never married, married, divorced or widowed). Categorical variables can be further categorised into ordinal and nominal variables. Ordinal categorical variable A categorical variable whose categories can be meaningfully ordered is called ordinal. For example, a student's grade in an exam (A, B, C or Fail) is ordinal.         Nominal categorical variable It does not matter which way the categories are ordered in tabular or graphical displays of the data -- all orderings are equally meaningful. For example, a student's religion (Atheist, Christian, Muslim, Hindu, ...) is nominal. Most statistical methods for categorical data can be applied to both ordinal and nominal variables.                                     Experimental Errors in Research Whilst many will not have heard of Type I error or Type II error, most people will be familiar with the terms 'false positive' and 'false negative', mainly as a medical term. A patient might take an HIV test, promising a 99.9% accuracy rate. This means that 1 in every 1000 tests could give a 'false positive,' informing a patient that they have the virus, when they do not. Conversely, the test could also show a false negative reading, giving an HIV positive patient the all-clear. This is why most medical tests require duplicate samples, to stack the odds up favorably. A one in one thousand chance becomes a 1 in 1 000 000 chance, if two independent  With any scientific process, there is no such ideal as total proof or total rejection, and researchers must, by necessity, work upon probabilities. That means that, whatever level of proof was reached, there is still the possibility that the results may be wrong. This could take the form of a false rejection, or acceptance, of the null hypothesis. How Does This Translate to Science Type I Error A Type I error is often referred to as a 'false positive', and is the process of incorrectly rejecting the null hypothesis in favor of the alternative. In the case above, the null hypothesis refers to the natural state of things, stating that the patient is not HIV positive. The alternative hypothesis states that the patient does carry the virus. A Type I error would indicate that the patient has the virus when they do not, a false rejection of the null. Type II Error A Type II error is the opposite of a Type I error and is the false acceptance of the null hypothesis. A Type II error, also known as a false negative, would imply that the patient is free of HIV when they are not, a dangerous diagnosis. In most fields of science, Type II errors are not seen to be as problematic as a Type I error. With the Type II error, a chance to reject the null hypothesis was lost, and no conclusion is inferred from a non-rejected null. The Type I error is more serious, because you have wrongly rejected the null hypothesis. Medicine, however, is one exception; telling a patient that they are free of disease, when they are not, is potentially dangerous. Replication This is the reason why scientific experiments must be replicatable, and other scientists must be able to follow the exact methodology. Even if the highest level of proof, where P < 0.01 (probability is less than 1%), is reached, out of every 100 experiments, there will be one false result. To a certain extent, duplicate or triplicate samples reduce the chance of error, but may still mask chance if the errorcausing variable is present in all samples. If however, other researchers, using the same equipment, replicate the experiment and find that the results are the same, the chances of 5 or 10 experiments giving false results is unbelievably small. This is how science regulates, and minimizes, the potential for Type I and Type II errors. Of course, in non-replicatable experiments and medical diagnosis, replication is not always possible, so the possibility of Type I and II errors is always a factor. One area that is guilty of ignoring Type I and II errors is the lawcourt, where the jury is not told that fingerprint and DNA tests may produce false results. There have been many documented miscarriages of justice involving these tests. Many courts will now not accept these tests alone, as proof of guilt, and require other evidence. Type III Errors Many statisticians are now adopting a third type of error, a type III, which is where the null hypothesis was rejected for the wrong reason. In an experiment, a researcher might postulate a hypothesis and perform research. After analyzing the results statistically, the null is rejected. The problem is, that there may be some relationship between the variables, but it could be for a different reason than stated in the hypothesis. An unknown process may underlie the relationship. Conclusion Both Type I errors and Type II errors are factors that every scientist and researcher must take into account. Whilst replication can minimize the chances of an inaccurate result, this is one of the major reasons why research should be replicatable. Many scientists do not accept quasi-experiments, because they are difficult to replicate and analyze.