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How do you know if you have enough information to draw an hypothesis test in statistics?
You can test a hypothesis with very little information. For hypothesis testing you will have a null hypothesis, and alternative and some test statistic. The hypothesis test consists of checking whether or not the test statistic lies in the critical region. If it does, then you reject the null hypothesis and accept the alternative. The default option is to stick with the null hypothesis.
If the number of observations is very small then the critical region is so small that you have virtually no chance of rejecting the null: you will default to accepting it.
Different test have different powers and these depend on the underlying distribution of the variable being tested as well as the sample size.
What else can I help you with?
Types of statistics?
There are two types of statistics. One is called descriptive statistics and the other is inferential statistics. Descriptive statistics is when you use numbers. Inferential statistics is when you draw conclusions or make predictions.
What general conclusion can you draw from the statistics?
That, very often, it depends who is paying for the statistics to be drawn up.
What are the steps for testing a hypothesis?
1)Ask a question 2)Make a hypothesis (predict what will happen with your experiment) 3)Research your hypothesis 4)Test your hypothesis 5)Collect/organized your data 6)Results 7)Draw a conclusion
How the role of statistics is used in research?
Statistics is used to design the experiment (what type of data needs to be obtained and how much), then statistics is used to analyze the data (make inferences and draw conclusions).
What are the advantages and disadvantages of data collection methods in statistics?
There are both advantages and disadvantages of data collection methods in statistics. The main advantages are the metrics and correlation one can draw from statistics. The disadvantages stem from sampling errors.
What is inferential data?
There is no inferential data. There is inferential statistics which from samples, you infer or draw a conclusion about the population. Hypothesis testing is an example of inferential statistics.
How do you draw a hypothesis from a review of literature?
One can draw a tentative hypothesis by clearly examining the varriables from the literature.
How can you tell if your hypothesis is reliable?
If the conclusion you draw from the data supports your hypothesis.
What term refers to information gathered to test a hypothesis?
The term is "data." Data is collected and analyzed to test a hypothesis and draw conclusions in scientific research and experiments.
How can statistics be utilized in a scientific study to analyze and interpret data effectively?
Statistics can be used in a scientific study to analyze and interpret data effectively by providing tools to summarize and make sense of the information collected. This includes techniques such as hypothesis testing, regression analysis, and significance testing, which help researchers draw conclusions and make informed decisions based on the data they have gathered.
What conclusion can you draw about a hypothesis if repeated trial do not support the hypothesis?
That its wrong (false).
What is the 6 steps of the scientific method?
1). identify the problem 2). collect information 3). make a hypothesis 4). test your hypothesis 5). record and analyze 6). draw a conclusion
Types of statistics?
There are two types of statistics. One is called descriptive statistics and the other is inferential statistics. Descriptive statistics is when you use numbers. Inferential statistics is when you draw conclusions or make predictions.
What general conclusion can you draw from the statistics?
That, very often, it depends who is paying for the statistics to be drawn up.
What can scientists do if the results of an experiment do not support a hypothesis?
draw conclusions
When you draw your final conclusion it must agree with your hypothesis?
it depends...
What can scientist do if the result of an experiment do not support a hypothesis?
draw conclusions
