Why statistical analysis

Most related: Descriptive statistics in R. Moreover, it helps in extracting distinct characteristics of data and in summarizing and explaining the essential features of data. The inferential statistical analysis basically is used when the inspection of each unit from the population is not achievable, hence, it extrapolates, the information obtained, to the complete population.

In simple words, inferential statistical analysis lets us test a hypothesis depending on a sample data from which we can extract inferences by applying probabilities and make generalizations about the whole data, and also can make conclusions with respect to future outcomes beyond the data available. By this way, it is highly preferable while drawing conclusions and making decisions about the whole population on the basis of sample data. As such, this method involves the sampling theory, various tests of significance, statistical control etc.

Predictive analysis is implemented to make a prediction of future events, or what is likely to take place next, based on current and past facts and figures. In simple terms, predictive analytics uses statistical techniques and machine learning algorithms to describe the possibility of future outcomes, behaviour, and trends depending on recent and previous data.

Widely used techniques under predictive analysis include data mining, data modelling, artificial intelligence, machine learning and etc. In the current business system, this analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing , and financial corporations, however, any business can take advantage of it by planning for an unpredictable future, such as to gain the competitive advantage and narrow down the risk connected with an unpredictable future event.

The predictive analysis converges on forecasting upcoming events using data and ascertaining the likelihood of several trends in data behaviour. The prescriptive analysis examines the data In order to find out what should be done, it is widely used in business analysis for identifying the best possible action for a situation.

While other statistical analysis might be deployed for driving exclusions, it provides the actual answer. Basically, it focuses on discovering the optimal suggestion for a process of decision making. Several techniques, implemented under prescriptive analysis are simulation, graph analysis, algorithms, complex event processing, machine learning, recommendation engine , business rules, etc.

However, it is nearly related to descriptive and predictive analysis, where descriptive analysis explains data in terms of what has happened, predictive analysis anticipates what could happen, and here prescriptive analysis deals in providing appropriate suggestions among the available preferences.

Exploratory data analysis , or EDA as it is known, is a counterpart of inferential statistics, and greatly implemented by data experts. It is generally the first step of the data analysis process that is conducted prior to any other statistical analysis techniques.

EDA is not deployed alone for predicting or generalizing, it renders a preview of data and assists in getting some key insights into it. This method fully focuses on analyzing patterns in the data to recognize potential relationships. It determines the statistical tests you can use to test your hypothesis later on. First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

Then, your participants will undergo a 5-minute meditation exercise. In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the change in math test scores from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. There are no dependent or independent variables in this study, because you only want to measure variables without influencing them in any way.

Measuring variables When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

Many variables can be measured at different levels of precision. For example, age data can be quantitative 8 years old or categorical young. If a variable is coded numerically e. Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures. You should aim for a sample that is representative of the population. In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces sampling bias and ensures that data from your sample is actually typical of the population.

Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling. While non-probability samples are more likely to be biased, they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population. Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section. Your participants are self-selected by their schools. Example: Sampling correlational study Your main population of interest is male college students in the US.

Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Your participants volunteer for the survey, making this a non-probability sample. Calculate sufficient sample size Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be e.

As a rule of thumb, a minimum of 30 units or more per subgroup is necessary. See an example. By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data. A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends. In contrast, a skewed distribution is asymmetric and has more values on one end than the other.

The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions. Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values. Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all. Measures of variability tell you how spread out the values in a data set are.

Four main measures of variability are often reported:. Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test. From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable.

Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics correlational study After collecting data from students, you tabulate descriptive statistics for annual parental income and GPA.

Next, we can compute a correlation coefficient and perform a statistical test to understand the significance of the relationship between the variables in the population. It should be easier to get rid of graphs and data so as not to clog up the UI so much. Minitab 18 review by Mike C. KNIME Analytics Platform works to be an open solution for data-driven innovation, helping users discover the potential hidden in data, mine for fresh insights, or predict new futures.

It boasts more than 2, modules, hundreds of ready-to-run examples, and a comprehensive range of integrated tools. KNIME is extremely helpful in evaluating big quantities of information with sophisticated algorithms and codes without programming since it utilizes block modules to accomplish graphical jobs. One downside is that it uses a bunch of memory on your desktop, which impacts the machine's general efficiency.

OriginPro is a user-friendly and easy-to-learn software application that provides data analysis and publication-quality graphing capabilities tailored to the needs of scientists and engineers. Those who use OriginPro can customize operations such as importing, graphing and analysis, all from the GUI. Graphs, analysis results, and reports update automatically when data or parameters change.

They are speedy and very helpful. The graphics that you can make are very professional, and always very aesthetically pleasing. Plus, you can do so much on one platform. I use it to analyze a variety of data and I never feel limited. The only thing is that there is a learning curve that is difficult to get over. OriginPro review by Louis C. I can easily seasonally adjust all data, forecast, and more. The customer support is one of the best I have ever experienced, which has been a huge benefit.

I have reached out with questions late on Sunday evenings and have gotten them answered immediately. Now that is customer support! NumXL review by Calen C. Users will also experience regular updates, a variety of ready-to-use statistical procedures, and a range of statistical methods. What I dislike is that there aren't enough help options available when one has questions about how to code for various statistical equations.

SAS Base is a programming language software that provides a web-based programming interface. It offers ready-to-use programs for data manipulation, information storage and retrieval, descriptive statistics, and reporting. This powerful data analysis tool also provides cross-platform and multi-platform support. You set up your data, write your code, run it, and then view the output or pass it to another program for further analysis. It does this quickly and efficiently once you have everything set up properly.

However, it can be a bit clunky to use - it feels somewhat dated as far as the interface is concerned and has a relatively steep learning curve. When your business is looking to evaluate models and formulas to find the relationship between variables, turn to statistical analysis.

No amount of data is too vast, especially with the endless amount that you can discover from it. Learn even more about what you can do with your data when you uncover the ins and outs of data mining.

She graduated with a Bachelor of Arts from Elmhurst College. In addition to working at G2, Mara is a freelance writer for a handful of small- and medium-sized tech companies. In her spare time, Mara is either at the gym, exploring the great outdoors with her rescue dog Zeke, enjoying Italian food, or right in the middle of a Harry Potter binge. Explore Topics Expand your knowledge. Curated Content Your time is valuable. G2 Community Guest Contributor Network.

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