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As the tools of statistics (programs and computing speeds) improve so does their relevance in social research. Statistics are used heavily in social research, from surveys to factor analysis to data mining.

Importance of statistics

The field of statistics is the science of learning from data. Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions. Statistics allows you to understand a subject much more deeply.

In this post, I cover two main reasons why studying the field of statistics is crucial in modern society. First, statisticians are guides for learning from data and navigating common problems that can lead you to incorrect conclusions. Second, given the growing importance of decisions and opinions based on data, its crucial that you can critically assess the quality of analyses that others present to you.

I think statistics is an exciting field with the thrill of discovering, learning, and challenging your assumptions. Statistics facilitates the creation of new knowledge. Bit by bit, we push back the frontier of what is known. To learn more about my passion for statistics as an experienced statistician, read more about me.

Everybody watches weather forecasting. Have you ever think how do you get that information? There are some computer models built on statistical concepts. These computer models compare prior weather with the current weather and predict future weather.
Statistics mostly used by the researcher. They use their statistical skills to collect the relevant data. Otherwise, it results in a loss of money, time, and data.
What do you understand by insurance? Everybody has some kind of insurance, whether it is medical, home, or any other insurance. Based on an individual application some businesses use statistical models to calculate the risk of giving insurance.
In the financial market also statistic plays a great role. Statistics are the key to how traders and businessmen invest and make money.
Statistics play a big role in the medical field. Before any drugs are prescribed, scientists must show a statistically valid rate of effectiveness. Statistics are behind all the studies of medicine.
Statistical concepts are used in quality testing. Companies make many products daily and every company should make sure that they sell the best quality items. However, companies cannot test all the products, so they use statistics samples.
In everyday life we make many predictions. For example, we keep the alarm for the morning when we dont know whether we will be alive in the morning or not. Here we use statistics basics to make predictions.
Doctors predict disease based on statistics concepts. Suppose a survey shows that 75%-80% of people have cancer and are not able to find the reason. When the statistics become involved, then you can have a better idea of how the cancer may affect your body or if smoking is the major reason for it.
A news reporter predicts the winner of elections based on political campaigns. Here statistics play a strong part in who will be your government.
Statistics data allow us to collect information around the world. The internet is a device which helps us to collect information. The fundamentals behind the internet are based on statistics and mathematics concepts.

Statistics Uses

Statistics are not just numbers and facts. You know, things like 4 out of 5 dentists prefer a specific toothpaste. Instead, its an array of knowledge and procedures that allow you to learn from data reliably. Statistics allow you to evaluate claims based on quantitative evidence and help you differentiate between reasonable and dubious conclusions. That aspect is particularly vital these days because data are so plentiful along with interpretations presented by people with unknown motivations.

Statisticians offer critical guidance in producing trustworthy analyses and predictions. Along the way, statisticians can help investigators avoid a wide variety of analytical traps.

When analysts use statistical procedures correctly, they tend to produce accurate results. Statistical analyses account for uncertainty and error in the results. Statisticians ensure that all aspects of a study follow the appropriate methods to produce trustworthy results. These methods include:

Producing reliable data.
Analyzing the data appropriately.
Drawing reasonable conclusions.

Variations

There may be some benefit in thinking more widely about this and, in particular, in thinking about different sources of variation. The OP writes of ‘two bacterial groups’, and one can suppose that there is a whole collection of ‘bacterial groups’ in the background that are known to be truly different groups … this then leads to the idea of a typical distance between two randomly selected groups, or the ‘between-group variation’.In contrast, there is the idea that one will get different measurements on sampling from members of the same group, which would be ‘within-group variation’. A typical statistical test would be based on the sampling error of the difference between two means of samples from the same population (from the same group) and this is related to the ‘within-group variation’. A criterion of practical significance might reasonably be based on the size of the ‘between-group variation’.

One can imagine a whole suite of experiments set up to quantify both the ‘between-group variation’ and the ‘within-group variation’, and there is a whole set of statistical analyses for this purpose in the topic of ‘Analysis of Variance’. However, such a suite of experiments may be beyond your scope at present. Nevertheless, the separation at a thought level of the two sources of variation should still be useful to you. There are at least two possibilities:

(a) a thought experiment where you imagine infinitely many samples from the two groups …then how big a difference would you expect in the means of the two samples if the groups are different.?
(b) a more limited extended experiment and limited analysis, where you repeat the procedure using several pairs of groups that are known to be different, with samples of about the same size as you have. You then compare the size of the difference you see in the initial experiment with the differences found for pairs when you know the groups are different. You can then say that the difference found is either smaller than, or much the same size as, the differences found when the groups are different.

One basis of progress in this direction (there may be others), is that you have (or imagine you have) a whole population of ‘groups’ that are truly different. Then a reasonable question is whether the difference found is smaller than you would find for a pair randomly sampled from the population of different groups. A difficulty in this is how you define the population of ‘groups’ to be not unnecessarily too different from the groups you are considering.

Role in Research

Statistics play an important role in research of almost any kind because they deal with easily quantified data. When working in fields such as science or medicine, trials are needed, and experimental data has to be collected and analyzed. The study of statistics enables researchers to look at a large set of data and condense it into meaningful information.

Key aspects of statistics include finding averages/means, determining outliers on either end of a bell curve, and finding the range of quantities for set points. Also, statisticians make use of representative samples with proportionate demographics to determine factors that could affect a whole population. For example, a researcher might look for the average percentage of people who buy a particular medication, and then compare it to people who reported side effects after taking that medication. This would be used to determine the likelihood of suffering harmful side effects.

In addition to collecting and analyzing data, researchers use their skills in statistics and/or public speaking to present data in such a way as to elicit a specific response from the intended audience. The use of different types of charts or graphs makes the presentation of information more palatable and understandable for laypeople and professionals alike.

Variables

The variable is a characteristic that varies from one individual member of the population to another individual. Variables such as height and weight are measured by some type of scale, convey quantitative information, and are called as quantitative variables. Sex and eye color give qualitative information and are called qualitative variables.

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as whole numbers such as 0, 1, 2, 3,& (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are several episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood, and the temperature.

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median, and mode. Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables.

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean.

Inferential statistics

In inferential statistics, data are analyzed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects

Qualitative Variable

Qualitative variables, on the other hand, can be put in groups or categories. For this reason, they are also called categorical variables. Sometimes they are given discrete numerical values that are convenient for grouping. However, these numbers are not useful in doing calculations such as average or standard deviation. For example, sports teams are put into divisions, such as Division 1, Division 2, etc. But these numbers are simply used for grouping, and would not be used in doing calculations. This makes sports team divisions an example of a qualitative variable.

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