Much data from the real world is normal distributed , that is, a frequency curve, or a frequency distribution , which has the most frequent number near the middle. Many experiments rely on assumptions of a normal distribution. This is a reason why researchers very often measure the central tendency in statistical research, such as the mean arithmetic mean or geometric mean , median or mode.
The central tendency may give a fairly good idea about the nature of the data mean, median and mode shows the "middle value" , especially when combined with measurements on how the data is distributed. Scientists normally calculate the standard deviation to measure how the data is distributed.
But there are various methods to measure how data is distributed: However, the sampling distribution will not be normally distributed if the distribution is skewed naturally or has outliers often rare outcomes or measurement errors messing up the data. One example of a distribution which is not normally distributed is the F-distribution , which is skewed to the right. So, often researchers double check that their results are normally distributed using range, median and mode. How do we know whether a hypothesis is correct or not?
Why use statistics to determine this? Using statistics in research involves a lot more than make use of statistical formulas or getting to know statistical software. Making use of statistics in research basically involves. Statistics in research is not just about formulas and calculation. Many wrong conclusions have been conducted from not understanding basic statistical concepts.
Statistics inference helps us to draw conclusions from samples of a population. When conducting experiments , a critical part is to test hypotheses against each other. Thus, it is an important part of the statistics tutorial for the scientific method. Hypothesis testing is conducted by formulating an alternative hypothesis which is tested against the null hypothesis , the common view. The hypotheses are tested statistically against each other. The researcher can work out a confidence interval , which defines the limits when you will regard a result as supporting the null hypothesis and when the alternative research hypothesis is supported.
This means that not all differences between the experimental group and the control group can be accepted as supporting the alternative hypothesis - the result need to differ significantly statistically for the researcher to accept the alternative hypothesis.
This is done using a significance test another article. Caution though, data dredging , data snooping or fishing for data without later testing your hypothesis in a controlled experiment may lead you to conclude on cause and effect even though there is no relationship to the truth.
Depending on the hypothesis, you will have to choose between one-tailed and two tailed tests. Sometimes the control group is replaced with experimental probability - often if the research treats a phenomenon which is ethically problematic , economically too costly or overly time-consuming, then the true experimental design is replaced by a quasi-experimental approach. Often there is a publication bias when the researcher finds the alternative hypothesis correct, rather than having a "null result", concluding that the null hypothesis provides the best explanation.
If applied correctly, statistics can be used to understand cause and effect between research variables. It may also help identify third variables, although statistics can also be used to manipulate and cover up third variables if the person presenting the numbers does not have honest intentions or sufficient knowledge with their results.
Misuse of statistics is a common phenomenon, and will probably continue as long as people have intentions about trying to influence others. Proper statistical treatment of experimental data can thus help avoid unethical use of statistics.
Philosophy of statistics involves justifying proper use of statistics, ensuring statistical validity and establishing the ethics in statistics. Here is another great statistics tutorial which integrates statistics and the scientific method.
Statistical tests make use of data from samples. These results are then generalized to the general population. How can we know that it reflects the correct conclusion? Contrary to what some might believe, errors in research are an essential part of significance testing. Ironically, the possibility of a research error is what makes the research scientific in the first place. If a hypothesis cannot be falsified e. If a hypothesis is testable, to be open to the possibility of going wrong.
Statistically this opens up the possibility of getting experimental errors in your results due to random errors or other problems with the research. ROC Curves are used to calculate sensitivity between true positives and false positives. Are there any websites that help you learn statistics? Are you sure you want to delete this answer?
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