95% confidence interval Flashcards
Summary Statistics
Summary statistics summarise and provide information about your sample data. This will tell you about the values in your data set. Summary statistics fall into three main categories:
Measures of location (also called central tendency).
Measures of spread.
Graphs/charts.
Your aim therefore is to present sample data using graphs and descriptive measures to summarise points and characteristics in the sample.
Inferential Statistics
Inferential statistics is used to make inferences about the characteristics of a populations based on sample data.
The goal is to go beyond the data at hand and make inferences about population parameters.
In order to use inferential statistics, it is assumed that either random selection or random assignment was carried out (i.e., some form of randomisation must is assumed)
Hypothesis testing:
How well the sample data supports important research question or claim concerning parameter in the study population.
Confidence interval
Calculate boundary values from the sample data that has known probability of capturing the parameter.
- Question
The sampling distribution for a sample mean does not approach normality (symmetry) when:
The population is approximately symmetric
The population is positively skewed
The sample size is very large
The sample size is small
The sample size is small
- Question
It is necessary to estimate the mean blood sugar level by drawing a sample from a large population of diabetic patients. The precision of the estimate will depend on:
The sample size
The mean sugar level in the population
The median of sugar level in the population
The population size
The sample size
Equality of variance
Evaluating equality of group variances is required when performing a hypothesis test for two groups. The degrees of freedom for T-score (T-test) and the SE depends on the equality of variances.
what is type l and type ll error?
Reject the null hypothesis when in reality the null is true – Type I error
Retain the null hypothesis when in reality the null is false – Type II error
Which of the following statements is incorrect regarding Type I and II errors?
Type I Error is rejection of the null hypothesis when in reality the null hypothesis is true
Type II Error is retaining the null hypothesis when in reality the null hypothesis is false
The power of the hypothesis test is the rejection of the null hypothesis when null is false.
Type I error is considered more serious than Type II error
Making type II error small involves rejecting the alternative hypothesis more often.
Making type II error small involves rejecting the alternative hypothesis more often
Which of the following statements regarding p value equalling 0.001 is/are INCORRECT?
This means that if 1000 similar studies were undertaken on the same population, only 1 out of 1000 studies would result in a sample result as extreme as the one obtained in the study is due to sampling variability or by chance.
This means that if 1000 similar studies were undertaken on the same population, only 999 out of 1000 studies would result in a sample result as extreme as the one obtained in the study is due to sampling variability or by chance.
The study result is so rare that a chance factor can be ignored for the difference from the hypothesized value.
The study result is so rare that a chance factor can’t be ignored for the difference from the hypothesized value.
The study result is so rare that a chance factor can’t be ignored for the difference from the hypothesized value.
This means that if 1000 similar studies were undertaken on the same population, only 999 out of 1000 studies would result in a sample result as extreme as the one obtained in the study is due to sampling variability or by chance.
