Summaries: the best scientific articles for research, science and statistics summarized

Introduction

With experiments we manipulate an assumed cause and then we observe which effects do follow. This is used for all modern scientific experiments, we try to discover the effects that a cause generates. It is also used in quasi-experiments, but in that context we have to think critically about causation. In a quasi-experiment there is no random assignment used to conditions, but they are carefully chosen.

An Example of a quasi-experiment

For example, in a quasi-experiment children were chosen for the control group. They tried to create a control group that is the same, as much as possible, as the treatment group.

Causation

In our daily lives we mostly intuitively recognize causal relationships. Nevertheless,

Introduction

According to Popper all theories are false, so this should mean that evaluating theories is very straightforward. But some theories are more false than others and some theories help in the advance of scientific knowledge because of their characteristics. A theory is a concise statement about how we believe the word to be. These theories are used to organize the observations of the word and give researchers the opportunity to make predictions about what will happen in the future. In recent years, more interest has grown towards how theories can be tested with formal models.

Criteria on which

The application of statistics to science is not a neutral act. Textbook writers in the social sceiences have transformed rivaling statistical systems into an apparently monolithic method that could be used mechaniscaly. No scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas.

If statisticans agree on one

Simpson (1951) showed that a statistical relationship observed in a population could be reversed within all the subgroups that make up that population. This has significant implications for medical and social sciences; because, a treatment that may seem effective at the population-level may, in fact, have adverse consequences within each of the population's subgroups. The Simpson's paradox (SP) has been formally analyzed by mathematicians and statisticians. But there hasn't been much work focused on the practical aspects of the SP for empirical science; how might the researchers prevent the paradox, recognize it, and deal with it upon detection?

In this paper they state that (a)

Introduction

A false positive is likely the most costly error that can be made in science. A false positive is the incorrect rejection of a null hypothesis.

Despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not.

Many researchers often stop collecting data on the basis of interim data analysis. Many researchers seem to believe that this practice exerts no more than a trivial influence on the false-positive rates.