More reason not to worry but say hello to stats!
To be able to use any body of knowledge we need to develop a unique outlook towards it. It is important to develop a statistical thinking so to say to be able to successfully understand and apply statistics in your work/research. To cut a long story short, you need to have a starting point of view.
Like any other discipline statistics helps solve problems through measurement. Though measurement does not answer all questions it does help us gain better understanding of most situations, which gives us more control over problems, a better handle to overcome them.
1) You as a medical researcher might want to determine the efficacy of a particular treatment or drug on a given ailment or medical condition. Herein you might want to measure the effect of a given treatment on a specified number of patients suffering from a given condition. This can give us a fair idea of the effectiveness of the given treatment. For this we need to collect data about the patient conditions, the dosage and mode of the drug/treatment, its timing and effects on each patient.
2) You as a researcher in Human Resources might want to measure the effect of a specific motivational plan on the employees in particular sector. You will collect the employee data as well as the data regarding the effect of the motivational plan on them.
3) As a market researcher you might want to establish the effect of an advertising program on the sales of a given brand or company. Herein you need the sales data prior to and post the running of the advertisement.
4) As a financial analyst you might want to collect financial data to measure the effect of a given investment action like buy/sell/hold on a specific stock or a portfolio of stocks over a period of time. You need the stock returns data for the said time period.
5) As a scientist on NASA's lunar program you might want to analyse the fuel consumption in different stages of a lunar mission to optimize the fuel consumption on the next mission.
You need data regarding the consumption of fuel in the different stages of the previous lunar missions.
The examples can be numerous and you can be overwhelmed by the sheer variety of situations where statistics can be applied, but an interesting starting point can be a set of these simple questions-
At this point it is important to understand that data does not always necessarily has to be measurable. There is something called qualitative data as well, which leads to information that is not measurable but is important and crucial nevertheless.
Watch video at following link to understand what I mean..
A good research has to consider all the data collection methods that might be relevant and crucial to the purpose and constraints of your research.
Since we are particularly concerned about statistics here, it is important to acknowledge the role of qualitative data in overall research and how it can useful to the statistical analysis that might required in your research.
Qualitative data often complements and supports the quantitative data measurement and statistical analysis of the same.
For instance in above examples,
1) In the medical research example, you may want to collect qualitative data about the signs, symptoms, experiences and related medical conditions of the patients.
2) In the market research example, you may want to explore the brand preferences of the customers to arrive at a starting point for defining the variables for quantitative research.
3) In the Financial analysis example, the scope of qualitative data is very limited, however nowadays a relatively new branch of finance namely behavioral finance might require the analyst to understand the human instincts of greed, fear and hope in a given context of the financial markets where the investment actions are being studied.
4) In the NASA example, the experiences of the astronauts can yield important information about optimization of fuel consumption in different stages as survival in space can be an important balancing or trade off factor for fuel consumption.
In the next post, we will talk more about data collection, till then try and ponder a little about what has been discussed so far...
2) You as a researcher in Human Resources might want to measure the effect of a specific motivational plan on the employees in particular sector. You will collect the employee data as well as the data regarding the effect of the motivational plan on them.
3) As a market researcher you might want to establish the effect of an advertising program on the sales of a given brand or company. Herein you need the sales data prior to and post the running of the advertisement.
4) As a financial analyst you might want to collect financial data to measure the effect of a given investment action like buy/sell/hold on a specific stock or a portfolio of stocks over a period of time. You need the stock returns data for the said time period.
5) As a scientist on NASA's lunar program you might want to analyse the fuel consumption in different stages of a lunar mission to optimize the fuel consumption on the next mission.
You need data regarding the consumption of fuel in the different stages of the previous lunar missions.
The examples can be numerous and you can be overwhelmed by the sheer variety of situations where statistics can be applied, but an interesting starting point can be a set of these simple questions-
- What is the purpose of the research?
- What is the data to be collected?
- Where to find it ?
- How to collect it?
At this point it is important to understand that data does not always necessarily has to be measurable. There is something called qualitative data as well, which leads to information that is not measurable but is important and crucial nevertheless.
Watch video at following link to understand what I mean..
A good research has to consider all the data collection methods that might be relevant and crucial to the purpose and constraints of your research.
Since we are particularly concerned about statistics here, it is important to acknowledge the role of qualitative data in overall research and how it can useful to the statistical analysis that might required in your research.
Qualitative data often complements and supports the quantitative data measurement and statistical analysis of the same.
For instance in above examples,
1) In the medical research example, you may want to collect qualitative data about the signs, symptoms, experiences and related medical conditions of the patients.
2) In the market research example, you may want to explore the brand preferences of the customers to arrive at a starting point for defining the variables for quantitative research.
3) In the Financial analysis example, the scope of qualitative data is very limited, however nowadays a relatively new branch of finance namely behavioral finance might require the analyst to understand the human instincts of greed, fear and hope in a given context of the financial markets where the investment actions are being studied.
4) In the NASA example, the experiences of the astronauts can yield important information about optimization of fuel consumption in different stages as survival in space can be an important balancing or trade off factor for fuel consumption.
In the next post, we will talk more about data collection, till then try and ponder a little about what has been discussed so far...
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