What is Statistical Analysis? definition and meaning
I think it is an excellent idea to explain what statistical analysis actually means in a simplified manner this may be a little too simplified. Your explanation of odds ratio is just fine however it is important to look at how and if the researchers normalized the data. If there was no normalization for things like other health issues, differences in how skilled the surgical teams were, or the relative “smoothness” of the procedure then it is possible that these or other factors are responsible for some or all of the observed differences in outcomes. A confidence interval is actually a probabilistic statement about the repeatability of the trial as a whole (with a different set of patients who meet the same criteria) so saying that the confidence interval is 0.4-0.6 at a 95% level is actually saying there is if they performed this trial over and over they estimate that 95% of the trials will produce a result that is between .4 and .6. I know it sounds like I just said what you did but in a more complicated way but the way answer B is phrased discounts the 5% chance that the trial is actually an outlier which would result in misleading conclusions being drawn from its result. As far as P Value goes you are correct that a P Value of .05 is frequently stated and it is indeed often the threshold for statistically significant; however “statistically significant;” is closer to a legal term than a statement of fact. A P-Value of .05 simply means that assuming the individual data points are Normally distributed (which they almost always are) then there is at most a 5% chance that there is no correlation between the manipulated variables and the results observed. I don’t know if this will help or just confuse people all over again because statistics consistently cause smart people to have a logical but incorrect understanding of what a set of statistics mean in a practical sense.
Some tips for when to use SPSS vs. SAS, FAQs about these statistical softwares, converting files amongst different stats programs, and tips for choosing what statistical analyses to run given your variable types as well as steps for doing the analyses in SPSS and SAS.
Once you have explored your data thoroughly, aided by visualisation and description techniques, you will need to identify what formal statistical analysis techniques (if any) you require to investigate the data further and to draw general conclusions from them. A very large number of statistical techniques have been developed to handle many different types of data and relationships between them, and it can be difficult and confusing to choose the correct techniques for a given set of data and the requirements of your investigation. Users sometimes drift into statistical inference, which includes standard errors, confidence limits, t-tests and chi-square tests, etc, without realising they are making a big step. In general, we would recommend that you seek advice from a competent statistician as to the most appropriate approach to formal analysis of your data. Further information can be found in some of the SSC , for example:What statistical analysis should I use? -- "shows general guidelines for choosing a statistical analysis....covers a number of common analyses and helps you choose among them based on the number of dependent variables (sometimes referred to as outcome variables), the nature of your independent variables (sometimes referred to as predictors). You also want to consider the nature of your dependent variable, namely whether it is an interval variable, ordinal or categorical variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables? for more information on this). The table then shows one or more statistical tests commonly used given these types of variables (but not necessarily the only type of test that could be used) and links showing how to do such tests using SAS, Stata and SPSS."First, knowing the level of measurement helps you decide how to interpret the data fromthat variable. When you know that a measure is nominal (like the one just described), thenyou know that the numerical values are just short codes for the longer names. Second,knowing the level of measurement helps you decide what statistical analysis is appropriateon the values that were assigned. If a measure is nominal, then you know that you wouldnever average the data values or do a t-test on the data.What does this have to do with BPM? How does this restaurant consistently achieve the level of service and quality of cuisine that a customer expects? Certainly the service and kitchen staff changed over that experience, for that matter it’s likely that the cooks and management changed several times as well. The one way that they could achieve this consistent quality is to have very well-defined processes that allow little or no variation, which can be benchmarked and measured. This type of benchmarking and measuring is what statistical analysis provides.